Show of Hands:Votes: Party classification:: Votes_Q1
bdanalytics
Date: (Fri) May 27, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret## Loading required package: lattice## Loading required package: ggplot2source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" # Done
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024"
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q1", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet") else
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Votes_Q1_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #"extract.features.end" #NULL #default: script will save envir at end of this chunk
#mysavChunk(glbOut$pfx, glbChunks[["last"]])
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#load("Votes_Q1_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.21 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 1 1938 Male Married (w/kids)
## 2 4 1970 Female over $150,000 Domestic Partners (w/kids)
## 3 5 1997 Male $75,000 - $100,000 Single (no kids)
## 4 8 1983 Male $100,001 - $150,000 Married (w/kids)
## 5 9 1984 Female $50,000 - $74,999 Married (w/kids)
## 6 10 1997 Female over $150,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1 Democrat No No No No
## 2 Bachelor's Degree Democrat Yes No No No
## 3 High School Diploma Republican Yes Yes No
## 4 Bachelor's Degree Democrat No Yes No Yes No
## 5 High School Diploma Republican No Yes No No No
## 6 Current K-12 Democrat No
## Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1 Yes Public No Yes No No No Yes
## 2 Yes Public No Yes No Yes No No Yes
## 3 Yes Private No No No Yes No No Yes
## 4 No Public No Yes No Yes No No Yes
## 5 Yes Public No Yes No Yes Yes No Yes
## 6 Yes Public No No No Yes No Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1 Try first No No Yes Yes
## 2 Science Study first Yes Yes No No Receiving No
## 3 Science Study first Yes No Yes Receiving No
## 4 Science Try first No Yes Yes No Giving Yes
## 5 Art Try first Yes No No No Giving No
## 6 Science Try first Yes Yes No Yes Receiving No
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 1 Yes Idealist No No Yes
## 2 No Pragmatist No No Cool headed Standard hours No
## 3 Yes Pragmatist No Yes Cool headed Odd hours No
## 4 No Idealist No No Cool headed Standard hours No
## 5 No Idealist Yes Yes Hot headed Standard hours No
## 6 No Pragmatist No No Standard hours
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1 Happy Yes Yes No No P.M. Yes Start Yes
## 2 Happy Yes Yes Yes No A.M. No End Yes
## 3 Right Yes No No Yes A.M. Yes Start Yes
## 4 Happy Yes Yes No No A.M. Yes Start Yes
## 5 Happy Yes Yes No Yes P.M. No End No
## 6
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1 No Circumstances Yes Yes Yes Yes No
## 2 No Me Yes Yes No Yes No Mysterious
## 3 Yes Circumstances No Yes No Yes Yes Mysterious
## 4 No Circumstances Yes No No Yes No TMI
## 5 No Me No Yes Yes Yes Yes TMI
## 6
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1 Yes Yes Talk Technology No No Yes
## 2 No No
## 3 No No Tunes Technology Yes Yes Yes Yes
## 4 No No Talk People No Yes Yes Yes
## 5 Yes No Tunes People No No Yes No
## 6
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1 No Demanding No No Cautious No Yes!
## 2 Mac Yes Cautious No Umm...
## 3 No Supportive No PC No Cautious No Umm...
## 4 Yes Supportive No Mac Yes Risk-friendly No Umm...
## 5 No Demanding Yes PC Yes Cautious No Yes!
## 6 Yes Supportive No PC
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1 No Space No In-person Yes No Yes
## 2 No Space Yes In-person No Yes Yes No
## 3 No Space No In-person No No Yes Yes
## 4 No Socialize Yes Online No Yes No Yes
## 5 No Socialize No Online No No Yes Yes
## 6 In-person No No Yes Yes
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people! Yes No Yes Yes No Yes
## 2 Yay people! Yes Yes Yes Yes Yes No Yes
## 3 Grrr people Yes No No No No No No
## 4 Grrr people No No Yes Yes No Yes Yes
## 5 Yay people! Yes No Yes Yes Yes Yes No
## 6 Grrr people Yes No Yes Yes No No Yes
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1 No No No Yes No Own Optimist
## 2
## 3 Yes No No Yes No Own Pessimist Mom
## 4 No No No Yes Yes Own Optimist Mom
## 5 No No Yes No No Own Optimist Mom
## 6 Yes Yes No Yes
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1 Yes Yes No No Nope Yes No No
## 2 No
## 3 No No No No Nope Yes No No No
## 4 No No No Yes Check! No No No Yes
## 5 No Yes Yes Yes Nope Yes No No Yes
## 6
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1 No Only-child No No Yes
## 2 No No Only-child Yes No No
## 3 Yes No Yes No Yes No
## 4 Yes No Yes No No Yes
## 5 No No Yes No No Yes
## 6
## USER_ID YOB Gender Income HouseholdStatus
## 193 245 1964 Male over $150,000 Married (w/kids)
## 848 1046 1953 Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836 3530 1995 Male Single (no kids)
## 4052 5050 1945 Female $75,000 - $100,000 Married (w/kids)
## 4093 5107 1980 Female $100,001 - $150,000 Married (w/kids)
## 5509 6888 1998 Female under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 193 Bachelor's Degree Republican Yes Yes No Yes
## 848 Democrat
## 2836 Current Undergraduate Democrat Yes Yes Yes No
## 4052 Bachelor's Degree Republican
## 4093 Bachelor's Degree Democrat No No
## 5509 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193 No Yes Public No Yes No Yes No
## 848
## 2836 Yes Public Yes No No Yes Yes
## 4052 No Public
## 4093 No No Private No
## 5509 Yes Yes
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 193 No Yes Science Try first Yes Yes Yes No
## 848
## 2836 Yes Yes Art Study first No Yes Yes
## 4052
## 4093 Yes
## 5509 Yes No Art Study first Yes No Yes No
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186
## 193 Giving Yes No Idealist Yes Yes Hot headed
## 848
## 2836 Yes Yes Idealist Yes No Cool headed
## 4052 No No No
## 4093 No No Pragmatist No Yes
## 5509 Giving No
## Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193 Standard hours No Happy Yes Yes No No
## 848
## 2836 Odd hours No Happy Yes Yes No
## 4052
## 4093
## 5509
## Q116197 Q115602 Q115777 Q115610 Q115611 Q115899 Q115390 Q114961
## 193 A.M. Yes End Yes Yes Me No No
## 848
## 2836 Yes End Yes No Circumstances Yes No
## 4052 P.M. Yes Start Yes No No
## 4093 P.M. Yes Start Yes No Circumstances
## 5509
## Q114748 Q115195 Q114517 Q114386 Q113992 Q114152 Q113583 Q113584
## 193 Yes No Yes TMI No Yes Tunes Technology
## 848
## 2836 Yes No No Mysterious No Yes Tunes People
## 4052 No Yes
## 4093 Tunes People
## 5509
## Q113181 Q112478 Q112512 Q112270 Q111848 Q111580 Q111220 Q110740
## 193 No Yes Yes Yes Supportive No Mac
## 848
## 2836 Yes Yes Yes No Yes Demanding Yes PC
## 4052
## 4093 Yes Supportive
## 5509
## Q109367 Q108950 Q109244 Q108855 Q108617 Q108856 Q108754
## 193 No Cautious No Yes! No Socialize No
## 848 Yes Risk-friendly Yes Yes! No Space No
## 2836 Yes Cautious Yes Yes
## 4052
## 4093 No Risk-friendly No Yes! No Space No
## 5509
## Q108342 Q108343 Q107869 Q107491 Q106993 Q106997 Q106272 Q106388
## 193 In-person No Yes Yes No Yay people! Yes Yes
## 848 In-person Yes
## 2836 In-person Yes Yes Yes No
## 4052 No Grrr people
## 4093 In-person Yes Yes Yes Yes Yay people! Yes Yes
## 5509
## Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193 No Yes No No Yes No No No
## 848
## 2836 Yes No No No Yes Yes No No
## 4052 No No No No
## 4093 No No No No Yes No No Yes
## 5509
## Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689 Q100680
## 193 No No Own Optimist Dad Yes Yes No
## 848
## 2836 Yes Yes Rent Optimist Dad No Yes Yes
## 4052 Yes Own No
## 4093 Yes Yes Rent No Yes
## 5509
## Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193 Yes Check! No No No Yes Yes No Yes
## 848
## 2836 Yes Check! No No No Yes Yes Yes
## 4052
## 4093 No Nope Yes No Yes Yes Yes No Yes
## 5509
## Q98078 Q98197 Q96024
## 193 No Yes Yes
## 848 No
## 2836 Yes Yes No
## 4052
## 4093 Yes Yes No
## 5509
## USER_ID YOB Gender Income HouseholdStatus
## 5563 6955 1966 Male over $150,000 Married (w/kids)
## 5564 6956 NA Male
## 5565 6957 2000 Female
## 5566 6958 1969 Male over $150,000
## 5567 6959 1986 Male $25,001 - $50,000 Married (w/kids)
## 5568 6960 1999 Male under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 5563 Bachelor's Degree Democrat
## 5564 Master's Degree Democrat No No
## 5565 Current K-12 Republican
## 5566 Bachelor's Degree Democrat Yes
## 5567 High School Diploma Republican
## 5568 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563 No Yes No Yes Yes
## 5564 No Yes Public Yes
## 5565 Public Yes
## 5566 No No No Yes Yes
## 5567 Yes Yes No
## 5568 Yes No No
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 5563
## 5564
## 5565 Yes Yes Art Try first No Yes Yes Yes
## 5566 Yes Yes Science
## 5567 No No Science No Yes
## 5568
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563
## 5564
## 5565 Receiving
## 5566
## 5567
## 5568
## Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## 'data.frame': 5568 obs. of 20 variables:
## $ USER_ID : int 1 4 5 8 9 10 11 12 13 15 ...
## $ YOB : int 1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
## $ Gender : chr "Male" "Female" "Male" "Male" ...
## $ Income : chr "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
## $ HouseholdStatus: chr "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
## $ EducationLevel : chr "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
## $ Party : chr "Democrat" "Democrat" "Republican" "Democrat" ...
## $ Q124742 : chr "No" "" "" "No" ...
## $ Q124122 : chr "" "Yes" "Yes" "Yes" ...
## $ Q123464 : chr "No" "No" "Yes" "No" ...
## $ Q123621 : chr "No" "No" "No" "Yes" ...
## $ Q122769 : chr "No" "No" "" "No" ...
## $ Q122770 : chr "Yes" "Yes" "Yes" "No" ...
## $ Q122771 : chr "Public" "Public" "Private" "Public" ...
## $ Q122120 : chr "No" "No" "No" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "No" "No" "No" "No" ...
## $ Q120978 : chr "" "Yes" "Yes" "Yes" ...
## $ Q121011 : chr "No" "No" "No" "No" ...
## $ Q120379 : chr "No" "No" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 20 variables:
## $ Q120650: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q118117: chr "Yes" "No" "Yes" "No" ...
## $ Q118233: chr "No" "No" "No" "No" ...
## $ Q118237: chr "No" "No" "Yes" "No" ...
## $ Q116441: chr "No" "Yes" "No" "No" ...
## $ Q116197: chr "P.M." "A.M." "A.M." "A.M." ...
## $ Q115611: chr "No" "No" "Yes" "No" ...
## $ Q115899: chr "Circumstances" "Me" "Circumstances" "Circumstances" ...
## $ Q115390: chr "Yes" "Yes" "No" "Yes" ...
## $ Q114748: chr "Yes" "No" "No" "No" ...
## $ Q115195: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q113584: chr "Technology" "" "Technology" "People" ...
## $ Q112478: chr "No" "" "Yes" "Yes" ...
## $ Q112270: chr "" "" "Yes" "Yes" ...
## $ Q111848: chr "No" "" "No" "Yes" ...
## $ Q106993: chr "Yes" "No" "Yes" "Yes" ...
## $ Q106388: chr "No" "Yes" "No" "No" ...
## $ Q105655: chr "No" "No" "No" "Yes" ...
## $ Q104996: chr "Yes" "Yes" "No" "Yes" ...
## $ Q102674: chr "No" "" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 21 variables:
## $ Q102674: chr "No" "" "No" "No" ...
## $ Q102687: chr "Yes" "" "Yes" "Yes" ...
## $ Q102289: chr "No" "" "No" "Yes" ...
## $ Q102089: chr "Own" "" "Own" "Own" ...
## $ Q101162: chr "Optimist" "" "Pessimist" "Optimist" ...
## $ Q101163: chr "" "" "Mom" "Mom" ...
## $ Q101596: chr "Yes" "" "No" "No" ...
## $ Q100689: chr "Yes" "" "No" "No" ...
## $ Q100680: chr "No" "" "No" "No" ...
## $ Q100562: chr "No" "" "No" "Yes" ...
## $ Q99982 : chr "Nope" "" "Nope" "Check!" ...
## $ Q100010: chr "Yes" "" "Yes" "No" ...
## $ Q99716 : chr "No" "" "No" "No" ...
## $ Q99581 : chr "No" "" "No" "No" ...
## $ Q99480 : chr "" "No" "No" "Yes" ...
## $ Q98869 : chr "No" "No" "Yes" "Yes" ...
## $ Q98578 : chr "" "No" "No" "No" ...
## $ Q98059 : chr "Only-child" "Only-child" "Yes" "Yes" ...
## $ Q98078 : chr "No" "Yes" "No" "No" ...
## $ Q98197 : chr "No" "No" "Yes" "No" ...
## $ Q96024 : chr "Yes" "No" "No" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 2 1985 Female $25,001 - $50,000 Single (no kids)
## 2 3 1983 Male $50,000 - $74,999 Married (w/kids)
## 3 6 1995 Male $75,000 - $100,000 Single (no kids)
## 4 7 1980 Female $50,000 - $74,999 Single (no kids)
## 5 14 1980 Female Married (no kids)
## 6 28 1973 Male over $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1 Master's Degree Yes No Yes No No
## 2 Current Undergraduate No Yes Yes
## 3 Current K-12
## 4 Master's Degree Yes Yes No Yes Yes Yes
## 5 Current Undergraduate Yes No Yes No No
## 6 Master's Degree No Yes No Yes No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1 Public No Yes Yes Yes No Yes Yes Science
## 2 Public No Yes No
## 3 No No No Yes No Yes Science
## 4 Public No Yes No Yes No Yes Yes Science
## 5 Public Yes Yes No Yes Yes No Yes Art
## 6 Public No Yes No Yes Yes Yes Yes Science
## Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first Yes Yes Yes No Giving Yes No
## 2 Study first No Yes No
## 3 Try first No Yes No Yes Giving
## 4 Try first Yes No No Yes Giving Yes Yes
## 5 Try first Yes Yes Yes Yes Giving No No
## 6 Try first Yes Yes No No Giving No Yes
## Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1 Idealist No Yes Cool headed Odd hours Yes Happy
## 2
## 3
## 4 Idealist No No Cool headed Standard hours No Happy
## 5 Idealist No Yes Hot headed Standard hours Yes Happy
## 6 Pragmatist Yes No Hot headed Odd hours Yes Right
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1 Yes Yes No Yes A.M. Yes End Yes No
## 2 Yes Yes P.M.
## 3 Yes
## 4 Yes No No Yes A.M. Yes Start Yes No
## 5 Yes Yes Yes No P.M. Yes End No No
## 6 Yes Yes Yes Yes P.M. End Yes Yes
## Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1 Me No Yes No Yes Yes TMI
## 2 No Yes
## 3 Yes No Yes Yes No TMI No
## 4 Me Yes No Yes Yes Yes TMI No
## 5 Me No No No Yes No TMI No
## 6 Circumstances No Yes No Yes No TMI Yes
## Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1 No Tunes People Yes Yes No Yes Yes
## 2 No No No Yes
## 3 No Tunes Technology Yes No Yes No
## 4 Yes Talk People No No Yes No Yes
## 5 Tunes Technology No Yes Yes Yes
## 6 No Talk Technology No Yes Yes No Yes
## Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855 Q108617
## 1 Supportive No Yes Cautious Yes Yes!
## 2 No Yes Cautious No Yes! No
## 3 No No No
## 4 Supportive No PC No Cautious Yes Yes! No
## 5 Supportive Yes Mac Yes Cautious No Yes! No
## 6 Demanding No PC Yes Cautious No Umm... No
## Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993 Q106997
## 1 Yes In-person Yes
## 2 Space No Yes Yes Yes Grrr people
## 3 Yes In-person No No Yes Yes Yay people!
## 4 Space No Online No No Yes Yes Yay people!
## 5 Space No In-person No No Yes No Grrr people
## 6 Space No In-person Yes Yes Yes Grrr people
## Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1
## 2 Yes No No Yes No Yes No No
## 3 Yes No Yes No No Yes Yes No No
## 4 No No No No No Yes Yes No No
## 5 No No No Yes Yes Yes Yes Yes No
## 6 Yes No Yes Yes No No No Yes Yes
## Q102674 Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689
## 1 No
## 2 Rent Pessimist Dad
## 3 No No Yes Own Optimist Mom No No
## 4 No No No Own Optimist Dad No No
## 5 Yes No No Own Pessimist Mom No Yes
## 6 Yes Yes No Own Pessimist Mom No Yes
## Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1 Yes Yes Yes
## 2 Yes Yes Yes
## 3 Yes Yes Nope No No No Yes Yes No Yes
## 4 Yes Yes Nope Yes No No No Yes No Yes
## 5 Yes Yes Nope Yes No No Yes No No Yes
## 6 Yes Yes Nope Yes No No Yes No No Yes
## Q98078 Q98197 Q96024
## 1
## 2 Yes No Yes
## 3 No Yes Yes
## 4 No No Yes
## 5 No No No
## 6 No No Yes
## USER_ID YOB Gender Income HouseholdStatus
## 503 2555 1956 Male over $150,000 Married (w/kids)
## 515 2616 1959 Male over $150,000 Married (w/kids)
## 857 4346 1990 Female $50,000 - $74,999
## 950 4814 1969 Male $75,000 - $100,000 Married (w/kids)
## 1207 6057 1937 Female $25,001 - $50,000 Married (no kids)
## 1255 6285 1976 Female $100,001 - $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503 Bachelor's Degree No No No Yes No Yes
## 515 Bachelor's Degree
## 857 Bachelor's Degree
## 950 Bachelor's Degree Yes No Yes No No
## 1207 Bachelor's Degree No Yes
## 1255 Bachelor's Degree
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503 Private No Yes No No Yes No Yes
## 515 No No
## 857 No Yes No No No No Yes
## 950 Public Yes Yes No Yes Yes No Yes
## 1207 Public No Yes No No No No
## 1255
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 503 Science Study first No Yes No Yes Giving Yes
## 515 Yes
## 857 Science Study first No No Yes No Receiving Yes
## 950 Science Study first No No No No Giving No
## 1207 Study first No No Yes Receiving Yes
## 1255
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 503 No Pragmatist No No Cool headed Standard hours No
## 515 No Pragmatist No Yes Cool headed Standard hours No
## 857 Yes Pragmatist No No Cool headed Odd hours No
## 950 No Pragmatist No Yes Hot headed Odd hours Yes
## 1207 No Pragmatist No No Hot headed No
## 1255
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503 Happy Yes Yes No No A.M. Yes End
## 515 Right Yes Yes No Yes Yes
## 857 Right Yes Yes No No A.M. Yes Start
## 950 Happy Yes Yes Yes No P.M. Yes Start
## 1207 Happy Yes Yes No No A.M. Yes Start
## 1255 Yes No Yes A.M. Yes Start
## Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503 Yes Yes Me No No No Yes Yes
## 515 Yes No Me Yes No Yes Yes No
## 857 Yes No Me No No No Yes
## 950 Yes No Me Yes No Yes No No
## 1207 No No Circumstances Yes No Yes No Yes
## 1255 Yes No Circumstances No Yes No Yes Yes
## Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512
## 503 TMI Yes Yes Tunes People Yes No Yes
## 515 No Yes Talk Technology
## 857 Mysterious No No Tunes People No No No
## 950 Mysterious No No Tunes People Yes Yes Yes
## 1207 Yes No Talk Yes
## 1255 TMI Yes Yes Yes
## Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 503 No Yes Demanding No PC No Cautious
## 515 No Yes No Mac Yes
## 857 Yes Yes Supportive No Mac No Risk-friendly
## 950 No Yes Supportive Yes PC No Cautious
## 1207 Supportive No PC Cautious
## 1255 Yes Yes Demanding No Mac
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 503 No Umm... No Space No In-person No Yes
## 515
## 857 Yes Umm... No Space No In-person No Yes
## 950 No Yes! No Space No In-person No No
## 1207 Yes! No Space No In-person No Yes
## 1255
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503 Yes Yes Yay people! Yes No No Yes No
## 515 No
## 857 No Yes Grrr people Yes No Yes No No
## 950 Yes No Grrr people Yes Yes No No No
## 1207 Yes Yes Yes
## 1255
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503 No Yes No No No Yes No Own
## 515 Yes Yes
## 857 No Yes Yes No No Yes Yes Own
## 950 Yes Yes Yes No No Yes No Own
## 1207 Yes
## 1255
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503 Pessimist Mom Yes Yes No Yes Check! Yes
## 515 Check! Yes
## 857 Optimist Mom No Yes Yes No Nope Yes
## 950 Pessimist Mom Yes No No No Check! Yes
## 1207
## 1255
## Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503 No No Yes Yes No Yes Yes Yes Yes
## 515 No Yes Yes Yes No Yes Yes
## 857 No Yes Yes Yes No Yes No No No
## 950 No No Yes Yes No Yes No Yes Yes
## 1207
## 1255
## USER_ID YOB Gender Income HouseholdStatus
## 1387 6922 1988 Male $50,000 - $74,999 Single (no kids)
## 1388 6928 1977 Female $50,000 - $74,999 Domestic Partners (no kids)
## 1389 6930 1998 Female $100,001 - $150,000 Single (no kids)
## 1390 6941 1989 Male $25,001 - $50,000 Married (no kids)
## 1391 6946 1996 Male
## 1392 6947 NA Female
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387 Master's Degree
## 1388 Master's Degree
## 1389 Current K-12 No No
## 1390 Bachelor's Degree
## 1391 Current K-12
## 1392 Yes Yes No No No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387 Yes Yes Yes Yes Yes Yes
## 1388 Yes No Yes
## 1389 Public Yes Yes Yes Yes Yes Yes Yes
## 1390 Yes Yes No No No
## 1391 Yes No No Yes No Yes Yes
## 1392 Public Yes Yes No Yes Yes Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science Try first No Yes Yes No Giving
## 1388 Art
## 1389 Art Study first Yes No Yes No Giving
## 1390
## 1391 Art Study first Yes Yes Yes No Giving
## 1392 Art No No No Yes Giving
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## 'data.frame': 1392 obs. of 20 variables:
## $ USER_ID : int 2 3 6 7 14 28 29 37 44 56 ...
## $ YOB : int 1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
## $ Gender : chr "Female" "Male" "Male" "Female" ...
## $ Income : chr "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
## $ HouseholdStatus: chr "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
## $ EducationLevel : chr "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
## $ Q124742 : chr "" "" "" "Yes" ...
## $ Q124122 : chr "Yes" "" "" "Yes" ...
## $ Q123464 : chr "No" "No" "" "No" ...
## $ Q123621 : chr "Yes" "" "" "Yes" ...
## $ Q122769 : chr "No" "Yes" "" "Yes" ...
## $ Q122770 : chr "No" "Yes" "" "Yes" ...
## $ Q122771 : chr "Public" "Public" "" "Public" ...
## $ Q122120 : chr "No" "No" "" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "Yes" "No" "No" "No" ...
## $ Q120978 : chr "Yes" "" "No" "Yes" ...
## $ Q121011 : chr "No" "" "Yes" "No" ...
## $ Q120379 : chr "Yes" "" "No" "Yes" ...
## $ Q120650 : chr "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 20 variables:
## $ Q120012: chr "Yes" "No" "No" "Yes" ...
## $ Q120014: chr "Yes" "Yes" "Yes" "No" ...
## $ Q118117: chr "No" "" "" "Yes" ...
## $ Q118237: chr "Yes" "" "" "No" ...
## $ Q116953: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q116601: chr "Yes" "Yes" "" "No" ...
## $ Q116448: chr "Yes" "" "" "Yes" ...
## $ Q116197: chr "A.M." "P.M." "" "A.M." ...
## $ Q115899: chr "Me" "" "" "Me" ...
## $ Q114961: chr "Yes" "" "No" "No" ...
## $ Q113584: chr "People" "" "Technology" "People" ...
## $ Q113181: chr "Yes" "No" "Yes" "No" ...
## $ Q112512: chr "No" "" "Yes" "Yes" ...
## $ Q108950: chr "Cautious" "Cautious" "" "Cautious" ...
## $ Q108617: chr "" "No" "No" "No" ...
## $ Q108342: chr "In-person" "" "In-person" "Online" ...
## $ Q107491: chr "" "Yes" "Yes" "Yes" ...
## $ Q106272: chr "" "Yes" "Yes" "No" ...
## $ Q106389: chr "" "No" "Yes" "No" ...
## $ Q104996: chr "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 21 variables:
## $ Q102674: chr "" "" "No" "No" ...
## $ Q102687: chr "" "" "No" "No" ...
## $ Q102289: chr "" "" "Yes" "No" ...
## $ Q102089: chr "" "Rent" "Own" "Own" ...
## $ Q101162: chr "" "Pessimist" "Optimist" "Optimist" ...
## $ Q101163: chr "" "Dad" "Mom" "Dad" ...
## $ Q101596: chr "" "" "No" "No" ...
## $ Q100689: chr "No" "" "No" "No" ...
## $ Q100680: chr "Yes" "" "Yes" "Yes" ...
## $ Q100562: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q99982 : chr "" "" "Nope" "Nope" ...
## $ Q100010: chr "" "" "No" "Yes" ...
## $ Q99716 : chr "" "" "No" "No" ...
## $ Q99581 : chr "" "" "No" "No" ...
## $ Q99480 : chr "" "" "Yes" "No" ...
## $ Q98869 : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q98578 : chr "" "" "No" "No" ...
## $ Q98059 : chr "" "Yes" "Yes" "Yes" ...
## $ Q98078 : chr "" "Yes" "No" "No" ...
## $ Q98197 : chr "" "No" "Yes" "No" ...
## $ Q96024 : chr "" "Yes" "Yes" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."
## [1] "Creating new feature: Hhold.fctr..."
## [1] "Creating new feature: Edn.fctr..."
## [1] "Creating new feature: Q124742.fctr..."
## [1] "Creating new feature: Q124122.fctr..."
## [1] "Creating new feature: Q123621.fctr..."
## [1] "Creating new feature: Q123464.fctr..."
## [1] "Creating new feature: Q122771.fctr..."
## [1] "Creating new feature: Q122770.fctr..."
## [1] "Creating new feature: Q122769.fctr..."
## [1] "Creating new feature: Q122120.fctr..."
## [1] "Creating new feature: Q121700.fctr..."
## [1] "Creating new feature: Q121699.fctr..."
## [1] "Creating new feature: Q121011.fctr..."
## [1] "Creating new feature: Q120978.fctr..."
## [1] "Creating new feature: Q120650.fctr..."
## [1] "Creating new feature: Q120472.fctr..."
## [1] "Creating new feature: Q120379.fctr..."
## [1] "Creating new feature: Q120194.fctr..."
## [1] "Creating new feature: Q120014.fctr..."
## [1] "Creating new feature: Q120012.fctr..."
## [1] "Creating new feature: Q119851.fctr..."
## [1] "Creating new feature: Q119650.fctr..."
## [1] "Creating new feature: Q119334.fctr..."
## [1] "Creating new feature: Q118892.fctr..."
## [1] "Creating new feature: Q118237.fctr..."
## [1] "Creating new feature: Q118233.fctr..."
## [1] "Creating new feature: Q118232.fctr..."
## [1] "Creating new feature: Q118117.fctr..."
## [1] "Creating new feature: Q117193.fctr..."
## [1] "Creating new feature: Q117186.fctr..."
## [1] "Creating new feature: Q116797.fctr..."
## [1] "Creating new feature: Q116881.fctr..."
## [1] "Creating new feature: Q116953.fctr..."
## [1] "Creating new feature: Q116601.fctr..."
## [1] "Creating new feature: Q116441.fctr..."
## [1] "Creating new feature: Q116448.fctr..."
## [1] "Creating new feature: Q116197.fctr..."
## [1] "Creating new feature: Q115602.fctr..."
## [1] "Creating new feature: Q115777.fctr..."
## [1] "Creating new feature: Q115610.fctr..."
## [1] "Creating new feature: Q115611.fctr..."
## [1] "Creating new feature: Q115899.fctr..."
## [1] "Creating new feature: Q115390.fctr..."
## [1] "Creating new feature: Q115195.fctr..."
## [1] "Creating new feature: Q114961.fctr..."
## [1] "Creating new feature: Q114748.fctr..."
## [1] "Creating new feature: Q114517.fctr..."
## [1] "Creating new feature: Q114386.fctr..."
## [1] "Creating new feature: Q114152.fctr..."
## [1] "Creating new feature: Q113992.fctr..."
## [1] "Creating new feature: Q113583.fctr..."
## [1] "Creating new feature: Q113584.fctr..."
## [1] "Creating new feature: Q113181.fctr..."
## [1] "Creating new feature: Q112478.fctr..."
## [1] "Creating new feature: Q112512.fctr..."
## [1] "Creating new feature: Q112270.fctr..."
## [1] "Creating new feature: Q111848.fctr..."
## [1] "Creating new feature: Q111580.fctr..."
## [1] "Creating new feature: Q111220.fctr..."
## [1] "Creating new feature: Q110740.fctr..."
## [1] "Creating new feature: Q109367.fctr..."
## [1] "Creating new feature: Q109244.fctr..."
## [1] "Creating new feature: Q108950.fctr..."
## [1] "Creating new feature: Q108855.fctr..."
## [1] "Creating new feature: Q108617.fctr..."
## [1] "Creating new feature: Q108856.fctr..."
## [1] "Creating new feature: Q108754.fctr..."
## [1] "Creating new feature: Q108342.fctr..."
## [1] "Creating new feature: Q108343.fctr..."
## [1] "Creating new feature: Q107869.fctr..."
## [1] "Creating new feature: Q107491.fctr..."
## [1] "Creating new feature: Q106993.fctr..."
## [1] "Creating new feature: Q106997.fctr..."
## [1] "Creating new feature: Q106272.fctr..."
## [1] "Creating new feature: Q106388.fctr..."
## [1] "Creating new feature: Q106389.fctr..."
## [1] "Creating new feature: Q106042.fctr..."
## [1] "Creating new feature: Q105840.fctr..."
## [1] "Creating new feature: Q105655.fctr..."
## [1] "Creating new feature: Q104996.fctr..."
## [1] "Creating new feature: Q103293.fctr..."
## [1] "Creating new feature: Q102906.fctr..."
## [1] "Creating new feature: Q102674.fctr..."
## [1] "Creating new feature: Q102687.fctr..."
## [1] "Creating new feature: Q102289.fctr..."
## [1] "Creating new feature: Q102089.fctr..."
## [1] "Creating new feature: Q101162.fctr..."
## [1] "Creating new feature: Q101163.fctr..."
## [1] "Creating new feature: Q101596.fctr..."
## [1] "Creating new feature: Q100689.fctr..."
## [1] "Creating new feature: Q100680.fctr..."
## [1] "Creating new feature: Q100562.fctr..."
## [1] "Creating new feature: Q100010.fctr..."## [1] "Partition stats:"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392
## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392## Loading required package: RColorBrewer
## .src .n
## 1 Train 5568
## 2 Test 1392## Loading required package: lazyeval## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.210 24.134 13.924
## 2 inspect.data 2 0 0 24.134 NA NAStep 2.0: inspect data
## Warning: Removed 1392 rows containing non-finite values (stat_count).## Loading required package: reshape2
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1392
## Train 2951 2617 NA
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1
## Train 0.5299928 0.4700072 NA
## [1] "numeric data missing in glbObsAll: "
## YOB
## 415
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## Party Party.fctr .n
## 1 Democrat D 2951
## 2 Republican R 2617
## 3 <NA> <NA> 1392## Warning: Removed 1 rows containing missing values (position_stack).
## Party.fctr.R Party.fctr.D Party.fctr.NA
## Test NA NA 1392
## Train 2617 2951 NA
## Party.fctr.R Party.fctr.D Party.fctr.NA
## Test NA NA 1
## Train 0.4700072 0.5299928 NA
## [1] "elapsed Time (secs): 8.577000"## Loading required package: caTools## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "elapsed Time (secs): 127.998000"
## [1] "elapsed Time (secs): 127.998000"## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 24.134 162.973 138.839
## 3 scrub.data 2 1 1 162.974 NA NAStep 2.1: scrub data
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 162.974 196.936 33.962
## 4 transform.data 2 2 2 196.937 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end
## 4 transform.data 2 2 2 196.937 196.98
## 5 extract.features 3 0 0 196.981 NA
## elapsed
## 4 0.043
## 5 NAStep 3.0: extract features
## label step_major step_minor label_minor bgn
## 5 extract.features 3 0 0 196.981
## 6 extract.features.datetime 3 1 1 197.003
## end elapsed
## 5 197.002 0.021
## 6 NA NAStep 3.1: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 197.031
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 6 extract.features.datetime 3 1 1 197.003
## 7 extract.features.image 3 2 2 197.042
## end elapsed
## 6 197.041 0.038
## 7 NA NAStep 3.2: extract features image
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 extract.features.image.bgn 1 0 0 197.076 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 197.076
## 2 extract.features.image.end 2 0 0 197.085
## end elapsed
## 1 197.085 0.009
## 2 NA NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 197.076
## 2 extract.features.image.end 2 0 0 197.085
## end elapsed
## 1 197.085 0.009
## 2 NA NA## label step_major step_minor label_minor bgn end
## 7 extract.features.image 3 2 2 197.042 197.095
## 8 extract.features.price 3 3 3 197.096 NA
## elapsed
## 7 0.054
## 8 NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 197.123 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 197.096 197.131
## 9 extract.features.text 3 4 4 197.132 NA
## elapsed
## 8 0.036
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 197.173 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 9 extract.features.text 3 4 4 197.132 197.2
## 10 extract.features.string 3 5 5 197.200 NA
## elapsed
## 9 0.068
## 10 NAStep 3.5: extract features string
## label step_major step_minor label_minor bgn
## 1 extract.features.string.bgn 1 0 0 197.233
## end elapsed
## 1 NA NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 197.233 197.244 0.012
## 2 0 197.245 NA NA## Gender Income HouseholdStatus EducationLevel
## "Gender" "Income" "HouseholdStatus" "EducationLevel"
## Party Q124742 Q124122 Q123464
## "Party" "Q124742" "Q124122" "Q123464"
## Q123621 Q122769 Q122770 Q122771
## "Q123621" "Q122769" "Q122770" "Q122771"
## Q122120 Q121699 Q121700 Q120978
## "Q122120" "Q121699" "Q121700" "Q120978"
## Q121011 Q120379 Q120650 Q120472
## "Q121011" "Q120379" "Q120650" "Q120472"
## Q120194 Q120012 Q120014 Q119334
## "Q120194" "Q120012" "Q120014" "Q119334"
## Q119851 Q119650 Q118892 Q118117
## "Q119851" "Q119650" "Q118892" "Q118117"
## Q118232 Q118233 Q118237 Q117186
## "Q118232" "Q118233" "Q118237" "Q117186"
## Q117193 Q116797 Q116881 Q116953
## "Q117193" "Q116797" "Q116881" "Q116953"
## Q116601 Q116441 Q116448 Q116197
## "Q116601" "Q116441" "Q116448" "Q116197"
## Q115602 Q115777 Q115610 Q115611
## "Q115602" "Q115777" "Q115610" "Q115611"
## Q115899 Q115390 Q114961 Q114748
## "Q115899" "Q115390" "Q114961" "Q114748"
## Q115195 Q114517 Q114386 Q113992
## "Q115195" "Q114517" "Q114386" "Q113992"
## Q114152 Q113583 Q113584 Q113181
## "Q114152" "Q113583" "Q113584" "Q113181"
## Q112478 Q112512 Q112270 Q111848
## "Q112478" "Q112512" "Q112270" "Q111848"
## Q111580 Q111220 Q110740 Q109367
## "Q111580" "Q111220" "Q110740" "Q109367"
## Q108950 Q109244 Q108855 Q108617
## "Q108950" "Q109244" "Q108855" "Q108617"
## Q108856 Q108754 Q108342 Q108343
## "Q108856" "Q108754" "Q108342" "Q108343"
## Q107869 Q107491 Q106993 Q106997
## "Q107869" "Q107491" "Q106993" "Q106997"
## Q106272 Q106388 Q106389 Q106042
## "Q106272" "Q106388" "Q106389" "Q106042"
## Q105840 Q105655 Q104996 Q103293
## "Q105840" "Q105655" "Q104996" "Q103293"
## Q102906 Q102674 Q102687 Q102289
## "Q102906" "Q102674" "Q102687" "Q102289"
## Q102089 Q101162 Q101163 Q101596
## "Q102089" "Q101162" "Q101163" "Q101596"
## Q100689 Q100680 Q100562 Q99982
## "Q100689" "Q100680" "Q100562" "Q99982"
## Q100010 Q99716 Q99581 Q99480
## "Q100010" "Q99716" "Q99581" "Q99480"
## Q98869 Q98578 Q98059 Q98078
## "Q98869" "Q98578" "Q98059" "Q98078"
## Q98197 Q96024 .src
## "Q98197" "Q96024" ".src"## label step_major step_minor label_minor bgn
## 10 extract.features.string 3 5 5 197.200
## 11 extract.features.end 3 6 6 197.269
## end elapsed
## 10 197.268 0.068
## 11 NA NAStep 3.6: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 11 extract.features.end 3 6 6 197.269 198.18
## 12 manage.missing.data 4 0 0 198.181 NA
## elapsed
## 11 0.912
## 12 NAStep 4.0: manage missing data
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## label step_major step_minor label_minor bgn end
## 12 manage.missing.data 4 0 0 198.181 199.078
## 13 cluster.data 5 0 0 199.079 NA
## elapsed
## 12 0.897
## 13 NAStep 5.0: cluster data
## label step_major step_minor label_minor bgn
## 13 cluster.data 5 0 0 199.079
## 14 partition.data.training 6 0 0 199.188
## end elapsed
## 13 199.188 0.109
## 14 NA NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.13 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.13 secs"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## [1] "lclgetMatrixCorrelation: duration: 40.914000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 14.304000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 50.199000 secs"
## [1] "cor of Fit vs. New: 1.0000"## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 106.67 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 1392
## Fit 2357 2091 NA
## OOB 594 526 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.5299011 0.4700989 NA
## OOB 0.5303571 0.4696429 NA
## Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6 SKn 1920 511 638 0.43165468 0.456250000
## 2 MKy 1296 298 371 0.29136691 0.266071429
## 1 MKn 516 136 169 0.11600719 0.121428571
## 3 N 367 83 102 0.08250899 0.074107143
## 7 SKy 147 53 65 0.03304856 0.047321429
## 4 PKn 150 30 37 0.03372302 0.026785714
## 5 PKy 52 9 10 0.01169065 0.008035714
## .freqRatio.Tst
## 6 0.458333333
## 2 0.266522989
## 1 0.121408046
## 3 0.073275862
## 7 0.046695402
## 4 0.026580460
## 5 0.007183908## [1] "glbObsAll: "## [1] 6960 209## [1] "glbObsTrn: "## [1] 5568 209## [1] "glbObsFit: "## [1] 4448 208## [1] "glbObsOOB: "## [1] 1120 208## [1] "glbObsNew: "## [1] 1392 208## [1] "partition.data.training chunk: teardown: elapsed: 107.54 secs"## label step_major step_minor label_minor bgn
## 14 partition.data.training 6 0 0 199.188
## 15 select.features 7 0 0 306.793
## end elapsed
## 14 306.792 107.604
## 15 NA NAStep 7.0: select features
## [1] "cor(Q108855.fctr, Q108856.fctr)=0.7430"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"
## [1] "cor(Party.fctr, Q108856.fctr)=-0.0140"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108856.fctr as highly correlated with Q108855.fctr## [1] "cor(Q122770.fctr, Q122771.fctr)=0.7379"
## [1] "cor(Party.fctr, Q122770.fctr)=-0.0195"
## [1] "cor(Party.fctr, Q122771.fctr)=-0.0348"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q122770.fctr as highly correlated with Q122771.fctr## [1] "cor(Q106272.fctr, Q106388.fctr)=0.7339"
## [1] "cor(Party.fctr, Q106272.fctr)=-0.0401"
## [1] "cor(Party.fctr, Q106388.fctr)=-0.0342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q106388.fctr as highly correlated with Q106272.fctr## [1] "cor(Q100680.fctr, Q100689.fctr)=0.7292"
## [1] "cor(Party.fctr, Q100680.fctr)=0.0158"
## [1] "cor(Party.fctr, Q100689.fctr)=0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100689.fctr## [1] "cor(Q120472.fctr, Q120650.fctr)=0.7126"
## [1] "cor(Party.fctr, Q120472.fctr)=-0.0462"
## [1] "cor(Party.fctr, Q120650.fctr)=-0.0271"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q120650.fctr as highly correlated with Q120472.fctr## [1] "cor(Q123464.fctr, Q123621.fctr)=0.7078"
## [1] "cor(Party.fctr, Q123464.fctr)=-0.0136"
## [1] "cor(Party.fctr, Q123621.fctr)=-0.0255"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q123464.fctr as highly correlated with Q123621.fctr## [1] "cor(Q108754.fctr, Q108855.fctr)=0.7005"
## [1] "cor(Party.fctr, Q108754.fctr)=-0.0081"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108754.fctr as highly correlated with Q108855.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## Q109244.fctr 0.1203812469 0 0.1203812469 <NA>
## Hhold.fctr 0.0511386673 0 0.0511386673 <NA>
## Edn.fctr 0.0359295351 0 0.0359295351 <NA>
## Q101163.fctr 0.0295046473 0 0.0295046473 <NA>
## Q100689.fctr 0.0256915080 0 0.0256915080 <NA>
## Q120379.fctr 0.0206291292 0 0.0206291292 <NA>
## Q121699.fctr 0.0196933075 0 0.0196933075 <NA>
## Q105840.fctr 0.0195569165 0 0.0195569165 <NA>
## Q113583.fctr 0.0191894717 0 0.0191894717 <NA>
## Q115195.fctr 0.0174522586 0 0.0174522586 <NA>
## Q102089.fctr 0.0174087944 0 0.0174087944 <NA>
## Q114386.fctr 0.0168013326 0 0.0168013326 <NA>
## Q100680.fctr 0.0157762454 0 0.0157762454 Q100689.fctr
## Q108342.fctr 0.0151842510 0 0.0151842510 <NA>
## Q111848.fctr 0.0141099384 0 0.0141099384 <NA>
## YOB.Age.fctr 0.0129198495 0 0.0129198495 <NA>
## Q118892.fctr 0.0125250379 0 0.0125250379 <NA>
## Q102687.fctr 0.0120079165 0 0.0120079165 <NA>
## Q115390.fctr 0.0119300319 0 0.0119300319 <NA>
## Q119851.fctr 0.0093381833 0 0.0093381833 <NA>
## Q114517.fctr 0.0084741753 0 0.0084741753 <NA>
## Q120012.fctr 0.0084652930 0 0.0084652930 <NA>
## Q109367.fctr 0.0080456026 0 0.0080456026 <NA>
## Q114961.fctr 0.0079206587 0 0.0079206587 <NA>
## Q121700.fctr 0.0067756198 0 0.0067756198 <NA>
## Q124122.fctr 0.0061257448 0 0.0061257448 <NA>
## Q111220.fctr 0.0055758571 0 0.0055758571 <NA>
## Q113992.fctr 0.0041479796 0 0.0041479796 <NA>
## Q121011.fctr 0.0037329030 0 0.0037329030 <NA>
## Q106042.fctr 0.0032327194 0 0.0032327194 <NA>
## Q116448.fctr 0.0031731051 0 0.0031731051 <NA>
## Q116601.fctr 0.0022379241 0 0.0022379241 <NA>
## Q104996.fctr 0.0012202806 0 0.0012202806 <NA>
## Q102906.fctr 0.0011540297 0 0.0011540297 <NA>
## Q113584.fctr 0.0011387024 0 0.0011387024 <NA>
## Q108950.fctr 0.0010567028 0 0.0010567028 <NA>
## Q102674.fctr 0.0009759844 0 0.0009759844 <NA>
## Q103293.fctr 0.0005915534 0 0.0005915534 <NA>
## Q112478.fctr 0.0001517248 0 0.0001517248 <NA>
## Q114748.fctr -0.0008477228 0 0.0008477228 <NA>
## Q107491.fctr -0.0014031814 0 0.0014031814 <NA>
## Q100562.fctr -0.0017132769 0 0.0017132769 <NA>
## Q108617.fctr -0.0024119725 0 0.0024119725 <NA>
## Q100010.fctr -0.0024291540 0 0.0024291540 <NA>
## Q115602.fctr -0.0027844465 0 0.0027844465 <NA>
## Q116953.fctr -0.0029786716 0 0.0029786716 <NA>
## Q115610.fctr -0.0035255582 0 0.0035255582 <NA>
## Q106997.fctr -0.0041749086 0 0.0041749086 <NA>
## Q120978.fctr -0.0044187616 0 0.0044187616 <NA>
## Q112512.fctr -0.0056768212 0 0.0056768212 <NA>
## Q108343.fctr -0.0060665340 0 0.0060665340 <NA>
## Q106389.fctr -0.0077498918 0 0.0077498918 <NA>
## .rnorm -0.0078039520 0 0.0078039520 <NA>
## Q108754.fctr -0.0080847764 0 0.0080847764 Q108855.fctr
## Q101162.fctr -0.0099412952 0 0.0099412952 <NA>
## Q115777.fctr -0.0101315203 0 0.0101315203 <NA>
## Q124742.fctr -0.0111642906 0 0.0111642906 <NA>
## Q116797.fctr -0.0112749656 0 0.0112749656 <NA>
## Q112270.fctr -0.0116157798 0 0.0116157798 <NA>
## YOB -0.0116828198 1 0.0116828198 <NA>
## Q118237.fctr -0.0117079669 0 0.0117079669 <NA>
## Q119650.fctr -0.0125645475 0 0.0125645475 <NA>
## Q111580.fctr -0.0132382335 0 0.0132382335 <NA>
## Q123464.fctr -0.0136140083 0 0.0136140083 Q123621.fctr
## Q117193.fctr -0.0138241599 0 0.0138241599 <NA>
## Q108856.fctr -0.0140363785 0 0.0140363785 Q108855.fctr
## Q118233.fctr -0.0147269325 0 0.0147269325 <NA>
## Q102289.fctr -0.0155850393 0 0.0155850393 <NA>
## Q116197.fctr -0.0158561766 0 0.0158561766 <NA>
## Income.fctr -0.0159635458 0 0.0159635458 <NA>
## Q118232.fctr -0.0171321152 0 0.0171321152 <NA>
## Q120194.fctr -0.0172986920 0 0.0172986920 <NA>
## Q114152.fctr -0.0175013163 0 0.0175013163 <NA>
## Q122770.fctr -0.0194639697 0 0.0194639697 Q122771.fctr
## Q117186.fctr -0.0198853672 0 0.0198853672 <NA>
## Q105655.fctr -0.0198994078 0 0.0198994078 <NA>
## Q106993.fctr -0.0207428635 0 0.0207428635 <NA>
## Q119334.fctr -0.0226894034 0 0.0226894034 <NA>
## Q122120.fctr -0.0229287700 0 0.0229287700 <NA>
## Q116441.fctr -0.0237358205 0 0.0237358205 <NA>
## Q118117.fctr -0.0253544150 0 0.0253544150 <NA>
## Q123621.fctr -0.0255329743 0 0.0255329743 <NA>
## Q122769.fctr -0.0259739146 0 0.0259739146 <NA>
## Q120650.fctr -0.0270889067 0 0.0270889067 Q120472.fctr
## .pos -0.0302037138 1 0.0302037138 <NA>
## USER_ID -0.0302304868 1 0.0302304868 <NA>
## Q107869.fctr -0.0304661021 0 0.0304661021 <NA>
## Q120014.fctr -0.0318620439 0 0.0318620439 <NA>
## Q115899.fctr -0.0324177950 0 0.0324177950 <NA>
## Q106388.fctr -0.0341579350 0 0.0341579350 Q106272.fctr
## Q122771.fctr -0.0348421015 0 0.0348421015 <NA>
## Q108855.fctr -0.0370970211 0 0.0370970211 <NA>
## Q110740.fctr -0.0380691243 0 0.0380691243 <NA>
## Q106272.fctr -0.0400926462 0 0.0400926462 <NA>
## Q101596.fctr -0.0409784077 0 0.0409784077 <NA>
## Q116881.fctr -0.0416860293 0 0.0416860293 <NA>
## Q120472.fctr -0.0462030674 0 0.0462030674 <NA>
## Q113181.fctr -0.0808753072 0 0.0808753072 <NA>
## Q115611.fctr -0.0904468203 0 0.0904468203 <NA>
## Gender.fctr -0.1027400851 0 0.1027400851 <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Q109244.fctr 1.125916 0.05387931 FALSE FALSE FALSE
## Hhold.fctr 1.525094 0.12571839 FALSE FALSE FALSE
## Edn.fctr 1.392610 0.14367816 FALSE FALSE FALSE
## Q101163.fctr 1.327394 0.05387931 FALSE FALSE FALSE
## Q100689.fctr 1.029800 0.05387931 FALSE FALSE FALSE
## Q120379.fctr 1.046326 0.05387931 FALSE FALSE FALSE
## Q121699.fctr 1.507127 0.05387931 FALSE FALSE FALSE
## Q105840.fctr 1.275362 0.05387931 FALSE FALSE FALSE
## Q113583.fctr 1.102515 0.05387931 FALSE FALSE FALSE
## Q115195.fctr 1.065496 0.05387931 FALSE FALSE FALSE
## Q102089.fctr 1.055963 0.05387931 FALSE FALSE FALSE
## Q114386.fctr 1.092072 0.05387931 FALSE FALSE FALSE
## Q100680.fctr 1.102386 0.05387931 FALSE FALSE FALSE
## Q108342.fctr 1.048292 0.05387931 FALSE FALSE FALSE
## Q111848.fctr 1.113602 0.05387931 FALSE FALSE FALSE
## YOB.Age.fctr 1.005794 0.16163793 FALSE FALSE FALSE
## Q118892.fctr 1.347380 0.05387931 FALSE FALSE FALSE
## Q102687.fctr 1.256545 0.05387931 FALSE FALSE FALSE
## Q115390.fctr 1.150505 0.05387931 FALSE FALSE FALSE
## Q119851.fctr 1.244519 0.05387931 FALSE FALSE FALSE
## Q114517.fctr 1.183374 0.05387931 FALSE FALSE FALSE
## Q120012.fctr 1.047185 0.05387931 FALSE FALSE FALSE
## Q109367.fctr 1.008571 0.05387931 FALSE FALSE FALSE
## Q114961.fctr 1.250436 0.05387931 FALSE FALSE FALSE
## Q121700.fctr 1.708221 0.05387931 FALSE FALSE TRUE
## Q124122.fctr 1.412807 0.05387931 FALSE FALSE TRUE
## Q111220.fctr 1.262849 0.05387931 FALSE FALSE TRUE
## Q113992.fctr 1.267442 0.05387931 FALSE FALSE TRUE
## Q121011.fctr 1.153676 0.05387931 FALSE FALSE TRUE
## Q106042.fctr 1.247738 0.05387931 FALSE FALSE TRUE
## Q116448.fctr 1.161031 0.05387931 FALSE FALSE TRUE
## Q116601.fctr 1.394914 0.05387931 FALSE FALSE TRUE
## Q104996.fctr 1.173840 0.05387931 FALSE FALSE TRUE
## Q102906.fctr 1.053396 0.05387931 FALSE FALSE TRUE
## Q113584.fctr 1.212486 0.05387931 FALSE FALSE TRUE
## Q108950.fctr 1.103872 0.05387931 FALSE FALSE TRUE
## Q102674.fctr 1.073412 0.05387931 FALSE FALSE TRUE
## Q103293.fctr 1.122287 0.05387931 FALSE FALSE TRUE
## Q112478.fctr 1.113648 0.05387931 FALSE FALSE TRUE
## Q114748.fctr 1.051125 0.05387931 FALSE FALSE TRUE
## Q107491.fctr 1.419021 0.05387931 FALSE FALSE TRUE
## Q100562.fctr 1.217215 0.05387931 FALSE FALSE TRUE
## Q108617.fctr 1.390618 0.05387931 FALSE FALSE TRUE
## Q100010.fctr 1.268156 0.05387931 FALSE FALSE TRUE
## Q115602.fctr 1.322302 0.05387931 FALSE FALSE TRUE
## Q116953.fctr 1.039180 0.05387931 FALSE FALSE TRUE
## Q115610.fctr 1.359695 0.05387931 FALSE FALSE TRUE
## Q106997.fctr 1.177632 0.05387931 FALSE FALSE TRUE
## Q120978.fctr 1.131963 0.05387931 FALSE FALSE TRUE
## Q112512.fctr 1.299253 0.05387931 FALSE FALSE TRUE
## Q108343.fctr 1.064910 0.05387931 FALSE FALSE TRUE
## Q106389.fctr 1.341307 0.05387931 FALSE FALSE TRUE
## .rnorm 1.000000 100.00000000 FALSE FALSE FALSE
## Q108754.fctr 1.008090 0.05387931 FALSE FALSE FALSE
## Q101162.fctr 1.103229 0.05387931 FALSE FALSE FALSE
## Q115777.fctr 1.140288 0.05387931 FALSE FALSE FALSE
## Q124742.fctr 2.565379 0.05387931 FALSE FALSE FALSE
## Q116797.fctr 1.009589 0.05387931 FALSE FALSE FALSE
## Q112270.fctr 1.254284 0.05387931 FALSE FALSE FALSE
## YOB 1.027559 1.41882184 FALSE FALSE FALSE
## Q118237.fctr 1.088017 0.05387931 FALSE FALSE FALSE
## Q119650.fctr 1.456978 0.05387931 FALSE FALSE FALSE
## Q111580.fctr 1.024977 0.05387931 FALSE FALSE FALSE
## Q123464.fctr 1.326681 0.05387931 FALSE FALSE FALSE
## Q117193.fctr 1.140665 0.05387931 FALSE FALSE FALSE
## Q108856.fctr 1.080645 0.05387931 FALSE FALSE FALSE
## Q118233.fctr 1.199142 0.05387931 FALSE FALSE FALSE
## Q102289.fctr 1.033482 0.05387931 FALSE FALSE FALSE
## Q116197.fctr 1.073778 0.05387931 FALSE FALSE FALSE
## Income.fctr 1.256724 0.12571839 FALSE FALSE FALSE
## Q118232.fctr 1.365812 0.05387931 FALSE FALSE FALSE
## Q120194.fctr 1.016716 0.05387931 FALSE FALSE FALSE
## Q114152.fctr 1.027617 0.05387931 FALSE FALSE FALSE
## Q122770.fctr 1.008802 0.05387931 FALSE FALSE FALSE
## Q117186.fctr 1.053878 0.05387931 FALSE FALSE FALSE
## Q105655.fctr 1.079316 0.05387931 FALSE FALSE FALSE
## Q106993.fctr 1.327392 0.05387931 FALSE FALSE FALSE
## Q119334.fctr 1.081498 0.05387931 FALSE FALSE FALSE
## Q122120.fctr 1.297443 0.05387931 FALSE FALSE FALSE
## Q116441.fctr 1.019645 0.05387931 FALSE FALSE FALSE
## Q118117.fctr 1.174006 0.05387931 FALSE FALSE FALSE
## Q123621.fctr 1.466381 0.05387931 FALSE FALSE FALSE
## Q122769.fctr 1.060606 0.05387931 FALSE FALSE FALSE
## Q120650.fctr 1.896247 0.05387931 FALSE FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE FALSE
## USER_ID 1.000000 100.00000000 FALSE FALSE FALSE
## Q107869.fctr 1.211050 0.05387931 FALSE FALSE FALSE
## Q120014.fctr 1.044944 0.05387931 FALSE FALSE FALSE
## Q115899.fctr 1.197849 0.05387931 FALSE FALSE FALSE
## Q106388.fctr 1.065033 0.05387931 FALSE FALSE FALSE
## Q122771.fctr 1.414753 0.05387931 FALSE FALSE FALSE
## Q108855.fctr 1.273980 0.05387931 FALSE FALSE FALSE
## Q110740.fctr 1.050779 0.05387931 FALSE FALSE FALSE
## Q106272.fctr 1.116536 0.05387931 FALSE FALSE FALSE
## Q101596.fctr 1.041667 0.05387931 FALSE FALSE FALSE
## Q116881.fctr 1.010066 0.05387931 FALSE FALSE FALSE
## Q120472.fctr 1.292633 0.05387931 FALSE FALSE FALSE
## Q113181.fctr 1.006354 0.05387931 FALSE FALSE FALSE
## Q115611.fctr 1.194859 0.05387931 FALSE FALSE FALSE
## Gender.fctr 1.561033 0.05387931 FALSE FALSE FALSE## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## [1] cor.y exclude.as.feat cor.y.abs cor.high.X
## [5] freqRatio percentUnique zeroVar nzv
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024 .lcn
## 2836 2858 1392## [1] "glb_feats_df:"## [1] 100 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID -0.03023049 TRUE 0.03023049 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID NA NA FALSE TRUE
## Party.fctr NA NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end
## 15 select.features 7 0 0 306.793 311.19
## 16 fit.models 8 0 0 311.190 NA
## elapsed
## 15 4.397
## 16 NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 311.686 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 311.686 311.718
## 2 fit.models_0_MFO 1 1 myMFO_classfr 311.719 NA
## elapsed
## 1 0.032
## 2 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.428000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
## D R
## 0.5299011 0.4700989
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.867000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.869000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## Prediction
## Reference R D
## R 2091 0
## D 2357 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4700989 0.0000000 0.4553427 0.4848945 0.5299011
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 3.814000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.429
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 0 1
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0.6395473 0.4700989
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4553427 0.4848945 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 0 1 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6391252 0.4696429
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4400805 0.4993651 0
## [1] "myfit_mdl: exit: 3.823000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 311.719
## 3 fit.models_0_Random 1 2 myrandom_classfr 315.547
## end elapsed
## 2 315.547 3.828
## 3 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.407000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.759000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.760000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 2091 0
## D 2357 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4700989 0.0000000 0.4553427 0.4848945 0.5299011
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 5.036000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.346 0.002 0.4942483
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.4619799 0.5265168 0.5073101 0.6
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6395473 0.4700989 0.4553427
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.4848945 0 0.523569 0.5
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.547138 0.5191202 0.6 0.6391252
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4696429 0.4400805 0.4993651
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 5.047000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 315.547 320.606 5.06
## 4 320.607 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.708000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 1.625000 secs"
## Length Class Mode
## a0 58 -none- numeric
## beta 232 dgCMatrix S4
## df 58 -none- numeric
## dim 2 -none- numeric
## lambda 58 -none- numeric
## dev.ratio 58 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrM Q109244.fctrNo Q109244.fctrYes
## 0.2665753 -0.2101506 -0.4308362 1.2139586
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Gender.fctrF" "Gender.fctrM" "Q109244.fctrNo"
## [5] "Q109244.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.723000 secs"
## Prediction
## Reference R D
## R 1950 141
## D 1762 595
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.721673e-01 1.772539e-01 5.574714e-01 5.867683e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 8.241814e-09 7.365212e-302
## Prediction
## Reference R D
## R 484 42
## D 447 147
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.633929e-01 1.605510e-01 5.337655e-01 5.926864e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.432605e-02 1.447405e-74
## [1] "myfit_mdl: predict complete: 4.401000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.907 0.064 0.5971118
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5480631 0.6461604 0.3580613 0.6
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6720662 0.5721673 0.5574714
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5867683 0.1772539 0.5896897 0.5228137
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6565657 0.3658672 0.6 0.6643789
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5633929 0.5337655 0.5926864
## max.Kappa.OOB
## 1 0.160551
## [1] "myfit_mdl: exit: 4.413000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.684000 secs"## Loading required package: rpart## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0225 on full training set
## [1] "myfit_mdl: train complete: 2.554000 secs"## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4448
##
## CP nsplit rel error
## 1 0.08990913 0 1.0000000
## 2 0.05930177 1 0.9100909
## 3 0.02247728 2 0.8507891
##
## Variable importance
## Q109244.fctrYes Q109244.fctrNo Gender.fctrM Gender.fctrF
## 83 15 1 1
##
## Node number 1: 4448 observations, complexity param=0.08990913
## predicted class=D expected loss=0.4700989 P(node) =1
## class counts: 2091 2357
## probabilities: 0.470 0.530
## left son=2 (3712 obs) right son=3 (736 obs)
## Primary splits:
## Q109244.fctrYes < 0.5 to the left, improve=136.83150, (0 missing)
## Q109244.fctrNo < 0.5 to the right, improve= 84.31128, (0 missing)
## Gender.fctrM < 0.5 to the right, improve= 24.39999, (0 missing)
## Gender.fctrF < 0.5 to the left, improve= 22.65952, (0 missing)
##
## Node number 2: 3712 observations, complexity param=0.05930177
## predicted class=R expected loss=0.4746767 P(node) =0.8345324
## class counts: 1950 1762
## probabilities: 0.525 0.475
## left son=4 (1980 obs) right son=5 (1732 obs)
## Primary splits:
## Q109244.fctrNo < 0.5 to the right, improve=24.259840, (0 missing)
## Gender.fctrM < 0.5 to the right, improve=10.189980, (0 missing)
## Gender.fctrF < 0.5 to the left, improve= 8.193561, (0 missing)
## Surrogate splits:
## Gender.fctrM < 0.5 to the right, agree=0.571, adj=0.080, (0 split)
## Gender.fctrF < 0.5 to the left, agree=0.563, adj=0.063, (0 split)
##
## Node number 3: 736 observations
## predicted class=D expected loss=0.1915761 P(node) =0.1654676
## class counts: 141 595
## probabilities: 0.192 0.808
##
## Node number 4: 1980 observations
## predicted class=R expected loss=0.4212121 P(node) =0.4451439
## class counts: 1146 834
## probabilities: 0.579 0.421
##
## Node number 5: 1732 observations
## predicted class=D expected loss=0.4642032 P(node) =0.3893885
## class counts: 804 928
## probabilities: 0.464 0.536
##
## n= 4448
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4448 2091 D (0.4700989 0.5299011)
## 2) Q109244.fctrYes< 0.5 3712 1762 R (0.5253233 0.4746767)
## 4) Q109244.fctrNo>=0.5 1980 834 R (0.5787879 0.4212121) *
## 5) Q109244.fctrNo< 0.5 1732 804 D (0.4642032 0.5357968) *
## 3) Q109244.fctrYes>=0.5 736 141 D (0.1915761 0.8084239) *
## [1] "myfit_mdl: train diagnostics complete: 3.394000 secs"
## Prediction
## Reference R D
## R 1950 141
## D 1762 595
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.721673e-01 1.772539e-01 5.574714e-01 5.867683e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 8.241814e-09 7.365212e-302
## Prediction
## Reference R D
## R 484 42
## D 447 147
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.633929e-01 1.605510e-01 5.337655e-01 5.926864e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.432605e-02 1.447405e-74
## [1] "myfit_mdl: predict complete: 6.085000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.862 0.019 0.5971118
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5480631 0.6461604 0.3676308 0.6
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6720662 0.600045 0.5574714
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5867683 0.1947896 0.5896897 0.5228137
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6565657 0.3774772 0.6 0.6643789
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5633929 0.5337655 0.5926864
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.160551 0.0124035 0.02559319
## [1] "myfit_mdl: exit: 6.100000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 320.607 331.158 10.551
## 5 331.158 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr"
## [1] "myfit_mdl: setup complete: 0.683000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.000115 on full training set
## [1] "myfit_mdl: train complete: 5.107000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 68 -none- numeric
## beta 2720 dgCMatrix S4
## df 68 -none- numeric
## dim 2 -none- numeric
## lambda 68 -none- numeric
## dev.ratio 68 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 40 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF
## 0.368820024 -0.163130387
## Gender.fctrM Q109244.fctrNo
## -0.313865523 -0.359818152
## Q109244.fctrYes Q109244.fctrNA:Q100689.fctrNo
## 1.135194801 0.264178396
## Q109244.fctrNo:Q100689.fctrNo Q109244.fctrYes:Q100689.fctrNo
## 0.270486538 -0.142734265
## Q109244.fctrNA:Q100689.fctrYes Q109244.fctrNo:Q100689.fctrYes
## 0.423668912 0.317723889
## Q109244.fctrYes:Q100689.fctrYes Q109244.fctrNA:Q106272.fctrNo
## 0.196982135 0.122953815
## Q109244.fctrNo:Q106272.fctrNo Q109244.fctrYes:Q106272.fctrNo
## 0.125251244 -0.196534927
## Q109244.fctrNA:Q106272.fctrYes Q109244.fctrNo:Q106272.fctrYes
## -0.225589882 -0.188148913
## Q109244.fctrNA:Q108855.fctrUmm... Q109244.fctrNo:Q108855.fctrUmm...
## -0.406823206 0.149840328
## Q109244.fctrYes:Q108855.fctrUmm... Q109244.fctrNA:Q108855.fctrYes!
## -0.041370823 0.041290322
## Q109244.fctrNo:Q108855.fctrYes! Q109244.fctrYes:Q108855.fctrYes!
## -0.121726184 -0.172888782
## Q109244.fctrNA:Q120472.fctrArt Q109244.fctrNo:Q120472.fctrArt
## 0.071673349 0.059590359
## Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience
## 0.228458668 -0.116255773
## Q109244.fctrNo:Q120472.fctrScience Q109244.fctrYes:Q120472.fctrScience
## -0.006126843 0.129080915
## Q109244.fctrNA:Q122771.fctrPc Q109244.fctrNo:Q122771.fctrPc
## 0.106934804 -0.198263813
## Q109244.fctrYes:Q122771.fctrPc Q109244.fctrNA:Q122771.fctrPt
## -0.302010432 -0.050475157
## Q109244.fctrNo:Q122771.fctrPt Q109244.fctrYes:Q122771.fctrPt
## -0.463540726 -0.592979565
## Q109244.fctrNA:Q123621.fctrNo Q109244.fctrNo:Q123621.fctrNo
## -0.146417007 0.037175018
## Q109244.fctrYes:Q123621.fctrNo Q109244.fctrNA:Q123621.fctrYes
## 0.609019125 -0.063916402
## Q109244.fctrNo:Q123621.fctrYes Q109244.fctrYes:Q123621.fctrYes
## -0.119295785 0.553231534
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] "Gender.fctrF"
## [3] "Gender.fctrM"
## [4] "Q109244.fctrNo"
## [5] "Q109244.fctrYes"
## [6] "Q109244.fctrNA:Q100689.fctrNo"
## [7] "Q109244.fctrNo:Q100689.fctrNo"
## [8] "Q109244.fctrYes:Q100689.fctrNo"
## [9] "Q109244.fctrNA:Q100689.fctrYes"
## [10] "Q109244.fctrNo:Q100689.fctrYes"
## [11] "Q109244.fctrYes:Q100689.fctrYes"
## [12] "Q109244.fctrNA:Q106272.fctrNo"
## [13] "Q109244.fctrNo:Q106272.fctrNo"
## [14] "Q109244.fctrYes:Q106272.fctrNo"
## [15] "Q109244.fctrNA:Q106272.fctrYes"
## [16] "Q109244.fctrNo:Q106272.fctrYes"
## [17] "Q109244.fctrYes:Q106272.fctrYes"
## [18] "Q109244.fctrNA:Q108855.fctrUmm..."
## [19] "Q109244.fctrNo:Q108855.fctrUmm..."
## [20] "Q109244.fctrYes:Q108855.fctrUmm..."
## [21] "Q109244.fctrNA:Q108855.fctrYes!"
## [22] "Q109244.fctrNo:Q108855.fctrYes!"
## [23] "Q109244.fctrYes:Q108855.fctrYes!"
## [24] "Q109244.fctrNA:Q120472.fctrArt"
## [25] "Q109244.fctrNo:Q120472.fctrArt"
## [26] "Q109244.fctrYes:Q120472.fctrArt"
## [27] "Q109244.fctrNA:Q120472.fctrScience"
## [28] "Q109244.fctrNo:Q120472.fctrScience"
## [29] "Q109244.fctrYes:Q120472.fctrScience"
## [30] "Q109244.fctrNA:Q122771.fctrPc"
## [31] "Q109244.fctrNo:Q122771.fctrPc"
## [32] "Q109244.fctrYes:Q122771.fctrPc"
## [33] "Q109244.fctrNA:Q122771.fctrPt"
## [34] "Q109244.fctrNo:Q122771.fctrPt"
## [35] "Q109244.fctrYes:Q122771.fctrPt"
## [36] "Q109244.fctrNA:Q123621.fctrNo"
## [37] "Q109244.fctrNo:Q123621.fctrNo"
## [38] "Q109244.fctrYes:Q123621.fctrNo"
## [39] "Q109244.fctrNA:Q123621.fctrYes"
## [40] "Q109244.fctrNo:Q123621.fctrYes"
## [41] "Q109244.fctrYes:Q123621.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 5.740000 secs"
## Prediction
## Reference R D
## R 1882 209
## D 1625 732
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.876799e-01 2.028604e-01 5.730476e-01 6.021969e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 5.298046e-15 2.076132e-239
## Prediction
## Reference R D
## R 488 38
## D 452 142
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.625000e-01 1.596227e-01 5.328684e-01 5.918025e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.666221e-02 1.098739e-77
## [1] "myfit_mdl: predict complete: 9.357000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.406 0.278
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6115819 0.5939742 0.6291896 0.3369481
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.672383 0.5996718
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5730476 0.6021969 0.1976596
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5920549 0.5494297 0.6346801 0.3664097
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6657572 0.5625
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5328684 0.5918025 0.1596227
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01526771 0.03220586
## [1] "myfit_mdl: exit: 9.371000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 331.158 340.554 9.396
## 6 340.555 NA NAindepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.703000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 19.985000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 88 -none- numeric
## beta 18656 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 212 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.1952047024 -0.1067097341
## Edn.fctr^6 Edn.fctr^7
## 0.0237553435 0.0749098536
## Gender.fctrM Hhold.fctrMKy
## -0.0703554818 -0.1490438784
## Hhold.fctrPKn Hhold.fctrSKn
## 0.5162299703 0.0279618972
## Hhold.fctrSKy Income.fctr.Q
## 0.1041010847 -0.0675479898
## Income.fctr.C Income.fctr^4
## -0.1206129633 -0.0139722858
## Income.fctr^6 Q100010.fctrNo
## 0.0007504834 0.0299022728
## Q100680.fctrYes Q100689.fctrYes
## 0.0032625549 0.1031391064
## Q101163.fctrDad Q101163.fctrMom
## -0.0883139744 0.1099784036
## Q102687.fctrYes Q103293.fctrYes
## 0.0312745394 0.0034287002
## Q104996.fctrNo Q104996.fctrYes
## -0.0237434125 0.0270717473
## Q105655.fctrYes Q106042.fctrNo
## -0.0412155561 -0.0205425586
## Q106272.fctrNo Q106272.fctrYes
## 0.0175158157 -0.0284921306
## Q106389.fctrNo Q106997.fctrGrrr people
## -0.0748652171 -0.0247097710
## Q106997.fctrYay people! Q107491.fctrYes
## 0.0764310291 0.0241770403
## Q108342.fctrOnline Q108855.fctrYes!
## 0.0659699015 -0.0544186322
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.0399793905 -0.3612442626
## Q109244.fctrYes Q110740.fctrMac
## 0.7867720593 0.0220394463
## Q110740.fctrPC Q111220.fctrYes
## -0.0875939927 0.0991012112
## Q111848.fctrYes Q112270.fctrYes
## 0.0228026313 0.0045878812
## Q112478.fctrNo Q113181.fctrNo
## -0.0523849496 0.1851568513
## Q113181.fctrYes Q113992.fctrYes
## -0.1950370446 0.0126219886
## Q114386.fctrMysterious Q115195.fctrYes
## 0.0019680292 0.0030704916
## Q115390.fctrNo Q115390.fctrYes
## -0.0745460116 0.0200284127
## Q115611.fctrNo Q115611.fctrYes
## 0.1339408085 -0.3306757390
## Q115899.fctrCircumstances Q115899.fctrMe
## 0.0851135127 -0.0130795183
## Q116197.fctrA.M. Q116881.fctrHappy
## -0.0262309208 0.0777873678
## Q116881.fctrRight Q116953.fctrNo
## -0.1304209856 -0.0263705676
## Q116953.fctrYes Q117186.fctrHot headed
## 0.0533044251 -0.0115004864
## Q118232.fctrIdealist Q118233.fctrNo
## 0.1031325558 -0.0082146917
## Q118233.fctrYes Q119650.fctrGiving
## 0.0137212711 -0.0170457793
## Q119851.fctrNo Q119851.fctrYes
## -0.1077857388 0.0180092566
## Q120012.fctrYes Q120014.fctrNo
## 0.0366851197 0.0336193579
## Q120014.fctrYes Q120194.fctrStudy first
## -0.0283086949 0.0593355542
## Q120379.fctrNo Q120379.fctrYes
## -0.0455168710 0.1013771165
## Q120472.fctrScience Q120650.fctrYes
## -0.0264043163 -0.0258801338
## Q121699.fctrNo Q121699.fctrYes
## -0.0654967987 0.0477679578
## Q121700.fctrNo Q121700.fctrYes
## -0.0073966532 0.0193609196
## Q122120.fctrYes Q122771.fctrPt
## -0.0342637586 -0.1211971800
## Q123464.fctrNo Q124122.fctrNo
## -0.0135208179 -0.0227693557
## Q124742.fctrNo YOB.Age.fctr.L
## 0.0271760998 0.1178489426
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.0091672083 0.0423673496
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.0067997116 -0.0389680361
## YOB.Age.fctr^8
## -0.0633534034
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.192887820 -0.119849973
## Edn.fctr^6 Edn.fctr^7
## 0.029850747 0.081133497
## Gender.fctrM Hhold.fctrMKy
## -0.070364216 -0.151860222
## Hhold.fctrPKn Hhold.fctrSKn
## 0.534578849 0.033885621
## Hhold.fctrSKy Income.fctr.Q
## 0.114752162 -0.071829866
## Income.fctr.C Income.fctr^4
## -0.130161899 -0.019455874
## Income.fctr^6 Q100010.fctrNo
## 0.006025901 0.036436326
## Q100680.fctrYes Q100689.fctrYes
## 0.004776233 0.110778663
## Q101163.fctrDad Q101163.fctrMom
## -0.093942855 0.110568655
## Q102687.fctrYes Q103293.fctrYes
## 0.035917303 0.009187328
## Q104996.fctrNo Q104996.fctrYes
## -0.027174935 0.030840441
## Q105655.fctrYes Q106042.fctrNo
## -0.047754402 -0.022572851
## Q106272.fctrNo Q106272.fctrYes
## 0.018750136 -0.033094547
## Q106389.fctrNo Q106997.fctrGrrr people
## -0.081395435 -0.028816760
## Q106997.fctrYay people! Q107491.fctrYes
## 0.081972579 0.029114800
## Q107869.fctrNo Q108342.fctrOnline
## 0.002204830 0.070638186
## Q108855.fctrYes! Q108950.fctrRisk-friendly
## -0.060370840 0.045240840
## Q109244.fctrNo Q109244.fctrYes
## -0.368068961 0.798909343
## Q110740.fctrMac Q110740.fctrPC
## 0.022194873 -0.094040223
## Q111220.fctrYes Q111848.fctrYes
## 0.105711882 0.026423743
## Q112270.fctrYes Q112478.fctrNo
## 0.011178188 -0.058869716
## Q113181.fctrNo Q113181.fctrYes
## 0.188992531 -0.200672215
## Q113992.fctrYes Q114386.fctrMysterious
## 0.018408206 0.008787278
## Q115195.fctrYes Q115390.fctrNo
## 0.006667928 -0.080608044
## Q115390.fctrYes Q115611.fctrNo
## 0.020849025 0.133196312
## Q115611.fctrYes Q115899.fctrCircumstances
## -0.339016722 0.089628166
## Q115899.fctrMe Q116197.fctrA.M.
## -0.014103408 -0.033977326
## Q116881.fctrHappy Q116881.fctrRight
## 0.081808928 -0.134553463
## Q116953.fctrNo Q116953.fctrYes
## -0.029073266 0.059821703
## Q117186.fctrHot headed Q118232.fctrIdealist
## -0.017114236 0.109817378
## Q118233.fctrNo Q118233.fctrYes
## -0.012865284 0.016738520
## Q119650.fctrGiving Q119851.fctrNo
## -0.022919294 -0.111428922
## Q119851.fctrYes Q120012.fctrYes
## 0.019736959 0.041230245
## Q120014.fctrNo Q120014.fctrYes
## 0.037876336 -0.030635600
## Q120194.fctrStudy first Q120379.fctrNo
## 0.064690194 -0.045827828
## Q120379.fctrYes Q120472.fctrScience
## 0.108653701 -0.028048835
## Q120650.fctrYes Q121699.fctrNo
## -0.031043954 -0.063199374
## Q121699.fctrYes Q121700.fctrNo
## 0.055354400 -0.011608644
## Q121700.fctrYes Q122120.fctrYes
## 0.019772249 -0.039779331
## Q122771.fctrPt Q123464.fctrNo
## -0.129074066 -0.018364756
## Q124122.fctrNo Q124122.fctrYes
## -0.026727216 0.001702308
## Q124742.fctrNo YOB.Age.fctr.L
## 0.034842422 0.133354527
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.022697089 0.051333522
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.014400447 -0.046938593
## YOB.Age.fctr^8
## -0.071194408
## [1] "myfit_mdl: train diagnostics complete: 20.665000 secs"
## Prediction
## Reference R D
## R 1854 237
## D 1480 877
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.139838e-01 2.503429e-01 5.994934e-01 6.283244e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 7.745054e-30 2.178832e-197
## Prediction
## Reference R D
## R 494 32
## D 466 128
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.553571e-01 1.476773e-01 5.256948e-01 5.847280e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 4.970426e-02 7.252253e-84
## [1] "myfit_mdl: predict complete: 27.999000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 19.107 1.869
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.645192 0.5805835 0.7098006 0.2855263
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.6835023 0.6218534
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5994934 0.6283244 0.2372918
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6261026 0.526616 0.7255892 0.3187291
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6648721 0.5553571
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5256948 0.584728 0.1476773
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004944753 0.0107377
## [1] "myfit_mdl: exit: 28.013000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 340.555 368.618
## 7 fit.models_0_end 1 6 teardown 368.619 NA
## elapsed
## 6 28.064
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 0 0 311.190 368.631 57.442
## 17 fit.models 8 1 1 368.632 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 372.924 NA NA# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indepVar <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
indepVar <- setdiff(indepVar, topindep_var)
if (length(interact_vars) > 0) {
indepVar <-
setdiff(indepVar, myextract_actual_feats(interact_vars))
indepVar <- c(indepVar,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indepVar <- union(indepVar, topindep_var)
}
}
if (is.null(indepVar))
indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indepVar))
indepVar <- mygetIndepVar(glb_feats_df)
if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {
indepVar <-
eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
}
indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indepVar <- setdiff(indepVar, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indepVar]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 372.924 372.934
## 2 fit.models_1_All.X 1 1 setup 372.935 NA
## elapsed
## 1 0.011
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 372.935 372.943
## 3 fit.models_1_All.X 1 2 glmnet 372.944 NA
## elapsed
## 2 0.008
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.709000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 19.889000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 88 -none- numeric
## beta 18656 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 212 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.1952047024 -0.1067097341
## Edn.fctr^6 Edn.fctr^7
## 0.0237553435 0.0749098536
## Gender.fctrM Hhold.fctrMKy
## -0.0703554818 -0.1490438784
## Hhold.fctrPKn Hhold.fctrSKn
## 0.5162299703 0.0279618972
## Hhold.fctrSKy Income.fctr.Q
## 0.1041010847 -0.0675479898
## Income.fctr.C Income.fctr^4
## -0.1206129633 -0.0139722858
## Income.fctr^6 Q100010.fctrNo
## 0.0007504834 0.0299022728
## Q100680.fctrYes Q100689.fctrYes
## 0.0032625549 0.1031391064
## Q101163.fctrDad Q101163.fctrMom
## -0.0883139744 0.1099784036
## Q102687.fctrYes Q103293.fctrYes
## 0.0312745394 0.0034287002
## Q104996.fctrNo Q104996.fctrYes
## -0.0237434125 0.0270717473
## Q105655.fctrYes Q106042.fctrNo
## -0.0412155561 -0.0205425586
## Q106272.fctrNo Q106272.fctrYes
## 0.0175158157 -0.0284921306
## Q106389.fctrNo Q106997.fctrGrrr people
## -0.0748652171 -0.0247097710
## Q106997.fctrYay people! Q107491.fctrYes
## 0.0764310291 0.0241770403
## Q108342.fctrOnline Q108855.fctrYes!
## 0.0659699015 -0.0544186322
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.0399793905 -0.3612442626
## Q109244.fctrYes Q110740.fctrMac
## 0.7867720593 0.0220394463
## Q110740.fctrPC Q111220.fctrYes
## -0.0875939927 0.0991012112
## Q111848.fctrYes Q112270.fctrYes
## 0.0228026313 0.0045878812
## Q112478.fctrNo Q113181.fctrNo
## -0.0523849496 0.1851568513
## Q113181.fctrYes Q113992.fctrYes
## -0.1950370446 0.0126219886
## Q114386.fctrMysterious Q115195.fctrYes
## 0.0019680292 0.0030704916
## Q115390.fctrNo Q115390.fctrYes
## -0.0745460116 0.0200284127
## Q115611.fctrNo Q115611.fctrYes
## 0.1339408085 -0.3306757390
## Q115899.fctrCircumstances Q115899.fctrMe
## 0.0851135127 -0.0130795183
## Q116197.fctrA.M. Q116881.fctrHappy
## -0.0262309208 0.0777873678
## Q116881.fctrRight Q116953.fctrNo
## -0.1304209856 -0.0263705676
## Q116953.fctrYes Q117186.fctrHot headed
## 0.0533044251 -0.0115004864
## Q118232.fctrIdealist Q118233.fctrNo
## 0.1031325558 -0.0082146917
## Q118233.fctrYes Q119650.fctrGiving
## 0.0137212711 -0.0170457793
## Q119851.fctrNo Q119851.fctrYes
## -0.1077857388 0.0180092566
## Q120012.fctrYes Q120014.fctrNo
## 0.0366851197 0.0336193579
## Q120014.fctrYes Q120194.fctrStudy first
## -0.0283086949 0.0593355542
## Q120379.fctrNo Q120379.fctrYes
## -0.0455168710 0.1013771165
## Q120472.fctrScience Q120650.fctrYes
## -0.0264043163 -0.0258801338
## Q121699.fctrNo Q121699.fctrYes
## -0.0654967987 0.0477679578
## Q121700.fctrNo Q121700.fctrYes
## -0.0073966532 0.0193609196
## Q122120.fctrYes Q122771.fctrPt
## -0.0342637586 -0.1211971800
## Q123464.fctrNo Q124122.fctrNo
## -0.0135208179 -0.0227693557
## Q124742.fctrNo YOB.Age.fctr.L
## 0.0271760998 0.1178489426
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.0091672083 0.0423673496
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.0067997116 -0.0389680361
## YOB.Age.fctr^8
## -0.0633534034
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.192887820 -0.119849973
## Edn.fctr^6 Edn.fctr^7
## 0.029850747 0.081133497
## Gender.fctrM Hhold.fctrMKy
## -0.070364216 -0.151860222
## Hhold.fctrPKn Hhold.fctrSKn
## 0.534578849 0.033885621
## Hhold.fctrSKy Income.fctr.Q
## 0.114752162 -0.071829866
## Income.fctr.C Income.fctr^4
## -0.130161899 -0.019455874
## Income.fctr^6 Q100010.fctrNo
## 0.006025901 0.036436326
## Q100680.fctrYes Q100689.fctrYes
## 0.004776233 0.110778663
## Q101163.fctrDad Q101163.fctrMom
## -0.093942855 0.110568655
## Q102687.fctrYes Q103293.fctrYes
## 0.035917303 0.009187328
## Q104996.fctrNo Q104996.fctrYes
## -0.027174935 0.030840441
## Q105655.fctrYes Q106042.fctrNo
## -0.047754402 -0.022572851
## Q106272.fctrNo Q106272.fctrYes
## 0.018750136 -0.033094547
## Q106389.fctrNo Q106997.fctrGrrr people
## -0.081395435 -0.028816760
## Q106997.fctrYay people! Q107491.fctrYes
## 0.081972579 0.029114800
## Q107869.fctrNo Q108342.fctrOnline
## 0.002204830 0.070638186
## Q108855.fctrYes! Q108950.fctrRisk-friendly
## -0.060370840 0.045240840
## Q109244.fctrNo Q109244.fctrYes
## -0.368068961 0.798909343
## Q110740.fctrMac Q110740.fctrPC
## 0.022194873 -0.094040223
## Q111220.fctrYes Q111848.fctrYes
## 0.105711882 0.026423743
## Q112270.fctrYes Q112478.fctrNo
## 0.011178188 -0.058869716
## Q113181.fctrNo Q113181.fctrYes
## 0.188992531 -0.200672215
## Q113992.fctrYes Q114386.fctrMysterious
## 0.018408206 0.008787278
## Q115195.fctrYes Q115390.fctrNo
## 0.006667928 -0.080608044
## Q115390.fctrYes Q115611.fctrNo
## 0.020849025 0.133196312
## Q115611.fctrYes Q115899.fctrCircumstances
## -0.339016722 0.089628166
## Q115899.fctrMe Q116197.fctrA.M.
## -0.014103408 -0.033977326
## Q116881.fctrHappy Q116881.fctrRight
## 0.081808928 -0.134553463
## Q116953.fctrNo Q116953.fctrYes
## -0.029073266 0.059821703
## Q117186.fctrHot headed Q118232.fctrIdealist
## -0.017114236 0.109817378
## Q118233.fctrNo Q118233.fctrYes
## -0.012865284 0.016738520
## Q119650.fctrGiving Q119851.fctrNo
## -0.022919294 -0.111428922
## Q119851.fctrYes Q120012.fctrYes
## 0.019736959 0.041230245
## Q120014.fctrNo Q120014.fctrYes
## 0.037876336 -0.030635600
## Q120194.fctrStudy first Q120379.fctrNo
## 0.064690194 -0.045827828
## Q120379.fctrYes Q120472.fctrScience
## 0.108653701 -0.028048835
## Q120650.fctrYes Q121699.fctrNo
## -0.031043954 -0.063199374
## Q121699.fctrYes Q121700.fctrNo
## 0.055354400 -0.011608644
## Q121700.fctrYes Q122120.fctrYes
## 0.019772249 -0.039779331
## Q122771.fctrPt Q123464.fctrNo
## -0.129074066 -0.018364756
## Q124122.fctrNo Q124122.fctrYes
## -0.026727216 0.001702308
## Q124742.fctrNo YOB.Age.fctr.L
## 0.034842422 0.133354527
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.022697089 0.051333522
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.014400447 -0.046938593
## YOB.Age.fctr^8
## -0.071194408
## [1] "myfit_mdl: train diagnostics complete: 20.655000 secs"
## Prediction
## Reference R D
## R 1854 237
## D 1480 877
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.139838e-01 2.503429e-01 5.994934e-01 6.283244e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 7.745054e-30 2.178832e-197
## Prediction
## Reference R D
## R 494 32
## D 466 128
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.553571e-01 1.476773e-01 5.256948e-01 5.847280e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 4.970426e-02 7.252253e-84
## [1] "myfit_mdl: predict complete: 28.143000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 19.069 1.889
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.645192 0.5805835 0.7098006 0.2855263
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.6835023 0.6218534
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5994934 0.6283244 0.2372918
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6261026 0.526616 0.7255892 0.3187291
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6648721 0.5553571
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5256948 0.584728 0.1476773
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004944753 0.0107377
## [1] "myfit_mdl: exit: 28.158000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 372.944 401.107
## 4 fit.models_1_All.X 1 3 glm 401.107 NA
## elapsed
## 3 28.163
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.684000 secs"
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 11.877000 secs"
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5129 -1.0543 0.4371 1.0404 2.1673
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.404600 0.258958 1.562 0.118191
## .rnorm -0.012902 0.033169 -0.389 0.697283
## Edn.fctr.L -0.059377 0.153969 -0.386 0.699764
## Edn.fctr.Q 0.005940 0.144395 0.041 0.967187
## Edn.fctr.C -0.026131 0.126132 -0.207 0.835874
## `Edn.fctr^4` -0.349477 0.125171 -2.792 0.005238 **
## `Edn.fctr^5` -0.062265 0.115375 -0.540 0.589421
## `Edn.fctr^6` 0.125644 0.104298 1.205 0.228330
## `Edn.fctr^7` 0.196465 0.115276 1.704 0.088326 .
## Gender.fctrF -0.384373 0.239985 -1.602 0.109232
## Gender.fctrM -0.454200 0.235784 -1.926 0.054062 .
## Hhold.fctrMKn 0.042781 0.178827 0.239 0.810928
## Hhold.fctrMKy -0.136663 0.164886 -0.829 0.407196
## Hhold.fctrPKn 0.946054 0.253546 3.731 0.000191 ***
## Hhold.fctrPKy 0.205624 0.336513 0.611 0.541171
## Hhold.fctrSKn 0.210213 0.141625 1.484 0.137732
## Hhold.fctrSKy 0.386839 0.243661 1.588 0.112374
## Income.fctr.L -0.087040 0.106772 -0.815 0.414959
## Income.fctr.Q -0.149860 0.097811 -1.532 0.125491
## Income.fctr.C -0.248123 0.095504 -2.598 0.009376 **
## `Income.fctr^4` -0.103826 0.092753 -1.119 0.262977
## `Income.fctr^5` 0.008740 0.094501 0.092 0.926312
## `Income.fctr^6` 0.090236 0.091902 0.982 0.326165
## Q100010.fctrNo 0.206870 0.167050 1.238 0.215577
## Q100010.fctrYes 0.119732 0.145306 0.824 0.409942
## Q100562.fctrNo 0.047558 0.196167 0.242 0.808441
## Q100562.fctrYes 0.027881 0.172432 0.162 0.871549
## Q100680.fctrNo -0.203518 0.190446 -1.069 0.285233
## Q100680.fctrYes -0.182237 0.184354 -0.989 0.322900
## Q100689.fctrNo 0.443233 0.191284 2.317 0.020496 *
## Q100689.fctrYes 0.587654 0.189916 3.094 0.001973 **
## Q101162.fctrOptimist 0.012706 0.176268 0.072 0.942537
## Q101162.fctrPessimist 0.024758 0.182085 0.136 0.891846
## Q101163.fctrDad -0.225265 0.156223 -1.442 0.149317
## Q101163.fctrMom 0.080249 0.160464 0.500 0.617001
## Q101596.fctrNo -0.444081 0.160115 -2.774 0.005545 **
## Q101596.fctrYes -0.420237 0.169044 -2.486 0.012920 *
## Q102089.fctrOwn 0.135326 0.161333 0.839 0.401582
## Q102089.fctrRent 0.104769 0.170542 0.614 0.538995
## Q102289.fctrNo 0.083146 0.164966 0.504 0.614251
## Q102289.fctrYes 0.001825 0.175851 0.010 0.991719
## Q102674.fctrNo -0.439035 0.213308 -2.058 0.039569 *
## Q102674.fctrYes -0.406335 0.224403 -1.811 0.070182 .
## Q102687.fctrNo 0.438231 0.227210 1.929 0.053762 .
## Q102687.fctrYes 0.491762 0.225761 2.178 0.029388 *
## Q102906.fctrNo 0.052032 0.167165 0.311 0.755604
## Q102906.fctrYes 0.022254 0.171654 0.130 0.896848
## Q103293.fctrNo -0.093048 0.150654 -0.618 0.536821
## Q103293.fctrYes 0.034808 0.152594 0.228 0.819559
## Q104996.fctrNo -0.024221 0.140863 -0.172 0.863477
## Q104996.fctrYes 0.136285 0.139161 0.979 0.327415
## Q105655.fctrNo -0.105306 0.171385 -0.614 0.538923
## Q105655.fctrYes -0.211229 0.169321 -1.248 0.212213
## Q105840.fctrNo 0.088515 0.172747 0.512 0.608374
## Q105840.fctrYes 0.073806 0.173692 0.425 0.670891
## Q106042.fctrNo -0.198059 0.171140 -1.157 0.247153
## Q106042.fctrYes -0.165279 0.171603 -0.963 0.335473
## Q106272.fctrNo 0.139762 0.191733 0.729 0.466038
## Q106272.fctrYes -0.009718 0.178760 -0.054 0.956645
## Q106388.fctrNo -0.025456 0.210246 -0.121 0.903629
## Q106388.fctrYes -0.038330 0.222455 -0.172 0.863199
## Q106389.fctrNo -0.252656 0.208731 -1.210 0.226112
## Q106389.fctrYes -0.059304 0.210385 -0.282 0.778034
## Q106993.fctrNo -0.210646 0.212624 -0.991 0.321834
## Q106993.fctrYes -0.121628 0.188951 -0.644 0.519770
## `Q106997.fctrGrrr people` 0.020504 0.191050 0.107 0.914535
## `Q106997.fctrYay people!` 0.260833 0.194477 1.341 0.179854
## Q107491.fctrNo 0.121109 0.178761 0.677 0.498097
## Q107491.fctrYes 0.164824 0.136557 1.207 0.227434
## Q107869.fctrNo 0.025807 0.144122 0.179 0.857886
## Q107869.fctrYes -0.048863 0.144799 -0.337 0.735772
## `Q108342.fctrIn-person` 0.209445 0.172322 1.215 0.224201
## Q108342.fctrOnline 0.335074 0.182027 1.841 0.065651 .
## Q108343.fctrNo -0.036563 0.179054 -0.204 0.838196
## Q108343.fctrYes -0.124442 0.189858 -0.655 0.512180
## Q108617.fctrNo 0.065017 0.163619 0.397 0.691098
## Q108617.fctrYes -0.124073 0.203811 -0.609 0.542680
## Q108754.fctrNo 0.047888 0.184417 0.260 0.795117
## Q108754.fctrYes 0.053638 0.193147 0.278 0.781236
## Q108855.fctrUmm... -0.013429 0.208866 -0.064 0.948735
## `Q108855.fctrYes!` -0.158332 0.205212 -0.772 0.440379
## Q108856.fctrSocialize -0.210516 0.211325 -0.996 0.319167
## Q108856.fctrSpace -0.210701 0.197120 -1.069 0.285116
## Q108950.fctrCautious 0.136136 0.153648 0.886 0.375605
## `Q108950.fctrRisk-friendly` 0.255209 0.164905 1.548 0.121717
## Q109244.fctrNo -0.599513 0.146470 -4.093 4.26e-05 ***
## Q109244.fctrYes 0.891074 0.168470 5.289 1.23e-07 ***
## Q109367.fctrNo 0.114856 0.151809 0.757 0.449301
## Q109367.fctrYes 0.076756 0.145361 0.528 0.597473
## Q110740.fctrMac -0.011357 0.128395 -0.088 0.929518
## Q110740.fctrPC -0.225175 0.125166 -1.799 0.072017 .
## Q111220.fctrNo -0.005890 0.137586 -0.043 0.965851
## Q111220.fctrYes 0.195332 0.151003 1.294 0.195817
## Q111580.fctrDemanding -0.047743 0.151204 -0.316 0.752189
## Q111580.fctrSupportive -0.013204 0.141552 -0.093 0.925682
## Q111848.fctrNo 0.094984 0.149118 0.637 0.524144
## Q111848.fctrYes 0.125010 0.143627 0.870 0.384091
## Q112270.fctrNo 0.134066 0.140609 0.953 0.340354
## Q112270.fctrYes 0.208086 0.140614 1.480 0.138918
## Q112478.fctrNo -0.336368 0.171509 -1.961 0.049853 *
## Q112478.fctrYes -0.143906 0.165693 -0.869 0.385118
## Q112512.fctrNo 0.050808 0.181717 0.280 0.779786
## Q112512.fctrYes 0.008362 0.154782 0.054 0.956914
## Q113181.fctrNo 0.234637 0.132143 1.776 0.075794 .
## Q113181.fctrYes -0.318203 0.135424 -2.350 0.018790 *
## Q113583.fctrTalk 0.067798 0.194198 0.349 0.727000
## Q113583.fctrTunes 0.099924 0.186236 0.537 0.591583
## Q113584.fctrPeople -0.134425 0.190832 -0.704 0.481173
## Q113584.fctrTechnology -0.110083 0.189503 -0.581 0.561304
## Q113992.fctrNo 0.206162 0.153153 1.346 0.178263
## Q113992.fctrYes 0.294459 0.164385 1.791 0.073248 .
## Q114152.fctrNo -0.099529 0.149933 -0.664 0.506803
## Q114152.fctrYes -0.019169 0.161800 -0.118 0.905694
## Q114386.fctrMysterious 0.052465 0.151837 0.346 0.729693
## Q114386.fctrTMI -0.029074 0.155470 -0.187 0.851652
## Q114517.fctrNo 0.173841 0.165377 1.051 0.293176
## Q114517.fctrYes 0.180799 0.175543 1.030 0.303039
## Q114748.fctrNo -0.343176 0.175585 -1.954 0.050645 .
## Q114748.fctrYes -0.312489 0.173927 -1.797 0.072389 .
## Q114961.fctrNo 0.233922 0.167844 1.394 0.163411
## Q114961.fctrYes 0.225086 0.166736 1.350 0.177030
## Q115195.fctrNo 0.050777 0.164871 0.308 0.758099
## Q115195.fctrYes 0.093288 0.154971 0.602 0.547193
## Q115390.fctrNo -0.222548 0.148228 -1.501 0.133257
## Q115390.fctrYes -0.019208 0.138813 -0.138 0.889948
## Q115602.fctrNo 0.081320 0.191123 0.425 0.670482
## Q115602.fctrYes 0.185016 0.170758 1.083 0.278587
## Q115610.fctrNo -0.003961 0.201746 -0.020 0.984335
## Q115610.fctrYes -0.049047 0.178778 -0.274 0.783818
## Q115611.fctrNo -0.014545 0.187943 -0.077 0.938313
## Q115611.fctrYes -0.594723 0.193384 -3.075 0.002103 **
## Q115777.fctrEnd 0.017977 0.157850 0.114 0.909329
## Q115777.fctrStart 0.053616 0.153909 0.348 0.727569
## Q115899.fctrCircumstances 0.187578 0.156429 1.199 0.230477
## Q115899.fctrMe -0.001892 0.154074 -0.012 0.990202
## Q116197.fctrA.M. -0.391833 0.154428 -2.537 0.011170 *
## Q116197.fctrP.M. -0.278879 0.144021 -1.936 0.052821 .
## Q116441.fctrNo -0.160980 0.174560 -0.922 0.356423
## Q116441.fctrYes -0.092929 0.187661 -0.495 0.620462
## Q116448.fctrNo 0.171616 0.165829 1.035 0.300717
## Q116448.fctrYes 0.153260 0.167277 0.916 0.359561
## Q116601.fctrNo 0.218695 0.193630 1.129 0.258707
## Q116601.fctrYes 0.184643 0.165463 1.116 0.264458
## Q116797.fctrNo -0.156843 0.167139 -0.938 0.348037
## Q116797.fctrYes -0.201294 0.172288 -1.168 0.242664
## Q116881.fctrHappy 0.142298 0.162785 0.874 0.382038
## Q116881.fctrRight -0.189475 0.177404 -1.068 0.285501
## Q116953.fctrNo 0.019237 0.175674 0.110 0.912801
## Q116953.fctrYes 0.253306 0.165211 1.533 0.125220
## `Q117186.fctrCool headed` -0.009769 0.163015 -0.060 0.952212
## `Q117186.fctrHot headed` -0.102196 0.170946 -0.598 0.549955
## `Q117193.fctrOdd hours` 0.024491 0.159926 0.153 0.878290
## `Q117193.fctrStandard hours` -0.050223 0.152544 -0.329 0.741978
## Q118117.fctrNo -0.012588 0.147559 -0.085 0.932015
## Q118117.fctrYes 0.016563 0.149805 0.111 0.911962
## Q118232.fctrIdealist 0.424880 0.145910 2.912 0.003592 **
## Q118232.fctrPragmatist 0.249776 0.144365 1.730 0.083599 .
## Q118233.fctrNo -0.135885 0.185318 -0.733 0.463402
## Q118233.fctrYes 0.027417 0.200906 0.136 0.891454
## Q118237.fctrNo -0.182864 0.187701 -0.974 0.329941
## Q118237.fctrYes -0.165963 0.184848 -0.898 0.369274
## Q118892.fctrNo 0.110529 0.131005 0.844 0.398840
## Q118892.fctrYes 0.063150 0.123630 0.511 0.609495
## Q119334.fctrNo -0.121554 0.134878 -0.901 0.367474
## Q119334.fctrYes -0.110868 0.131794 -0.841 0.400222
## Q119650.fctrGiving -0.141490 0.139945 -1.011 0.311999
## Q119650.fctrReceiving -0.012608 0.156611 -0.081 0.935834
## Q119851.fctrNo -0.181962 0.161364 -1.128 0.259467
## Q119851.fctrYes -0.007462 0.160769 -0.046 0.962982
## Q120012.fctrNo 0.072384 0.159828 0.453 0.650631
## Q120012.fctrYes 0.179611 0.158737 1.132 0.257844
## Q120014.fctrNo 0.021632 0.148794 0.145 0.884408
## Q120014.fctrYes -0.118054 0.141129 -0.836 0.402876
## `Q120194.fctrStudy first` 0.275430 0.136862 2.012 0.044171 *
## `Q120194.fctrTry first` 0.180054 0.142161 1.267 0.205317
## Q120379.fctrNo -0.051802 0.150377 -0.344 0.730485
## Q120379.fctrYes 0.211667 0.149287 1.418 0.156234
## Q120472.fctrArt -0.051686 0.153358 -0.337 0.736097
## Q120472.fctrScience -0.111310 0.143161 -0.778 0.436855
## Q120650.fctrNo -0.085901 0.194182 -0.442 0.658217
## Q120650.fctrYes -0.212186 0.142711 -1.487 0.137060
## Q120978.fctrNo 0.062449 0.155800 0.401 0.688547
## Q120978.fctrYes 0.070923 0.152319 0.466 0.641485
## Q121011.fctrNo 0.184986 0.156694 1.181 0.237780
## Q121011.fctrYes 0.141255 0.154185 0.916 0.359596
## Q121699.fctrNo 0.377420 0.238968 1.579 0.114251
## Q121699.fctrYes 0.573505 0.230376 2.489 0.012795 *
## Q121700.fctrNo -0.471980 0.232495 -2.030 0.042350 *
## Q121700.fctrYes -0.381078 0.251280 -1.517 0.129380
## Q122120.fctrNo -0.041853 0.137012 -0.305 0.760008
## Q122120.fctrYes -0.153346 0.150608 -1.018 0.308592
## Q122769.fctrNo -0.067759 0.206885 -0.328 0.743275
## Q122769.fctrYes -0.083726 0.209971 -0.399 0.690076
## Q122770.fctrNo 0.168409 0.253192 0.665 0.505958
## Q122770.fctrYes 0.153623 0.250000 0.614 0.538891
## Q122771.fctrPc -0.157444 0.232168 -0.678 0.497680
## Q122771.fctrPt -0.386119 0.246090 -1.569 0.116644
## Q123464.fctrNo -0.052050 0.157938 -0.330 0.741733
## Q123464.fctrYes 0.112459 0.230993 0.487 0.626364
## Q123621.fctrNo -0.080284 0.163157 -0.492 0.622674
## Q123621.fctrYes -0.062375 0.167728 -0.372 0.709981
## Q124122.fctrNo -0.042217 0.134780 -0.313 0.754107
## Q124122.fctrYes 0.085903 0.129422 0.664 0.506856
## Q124742.fctrNo 0.156242 0.103466 1.510 0.131024
## Q124742.fctrYes 0.009912 0.119612 0.083 0.933959
## YOB.Age.fctr.L 0.474767 0.188127 2.524 0.011614 *
## YOB.Age.fctr.Q 0.229265 0.156292 1.467 0.142403
## YOB.Age.fctr.C -0.057252 0.135106 -0.424 0.671743
## `YOB.Age.fctr^4` 0.211239 0.126263 1.673 0.094326 .
## `YOB.Age.fctr^5` 0.075605 0.116986 0.646 0.518104
## `YOB.Age.fctr^6` 0.133065 0.105371 1.263 0.206653
## `YOB.Age.fctr^7` -0.175589 0.099829 -1.759 0.078595 .
## `YOB.Age.fctr^8` -0.209542 0.102857 -2.037 0.041628 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6150.3 on 4447 degrees of freedom
## Residual deviance: 5378.0 on 4235 degrees of freedom
## AIC: 5804
##
## Number of Fisher Scoring iterations: 4
##
## [1] "myfit_mdl: train diagnostics complete: 13.658000 secs"
## Prediction
## Reference R D
## R 1719 372
## D 1198 1159
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.470324e-01 3.069788e-01 6.327828e-01 6.610886e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 1.956853e-56 2.785875e-96
## Prediction
## Reference R D
## R 457 69
## D 396 198
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.848214e-01 1.952083e-01 5.553253e-01 6.138708e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.400930e-04 1.234384e-51
## [1] "myfit_mdl: predict complete: 21.259000 secs"
## id
## 1 All.X##rcv#glm
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 11.096 1.313
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6722741 0.6432329 0.7013152 0.2700242
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.6865016 0.5999708
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6327828 0.6610886 0.1961948
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5998195 0.5380228 0.6616162 0.3425094
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6627991 0.5848214
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5553253 0.6138708 0.1952083
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.006222678 0.01300276
## [1] "myfit_mdl: exit: 21.273000 secs"# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 401.107 422.431
## 5 fit.models_1_preProc 1 4 preProc 422.432 NA
## elapsed
## 4 21.324
## 5 NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indepVar=indepVar, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indepVar
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indepVar_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indepVar_lst[["feat"]] <- feat
# User specified combinatorial models
# indepVar_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indepVar=indepVar,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr 0 0.429
## Random###myrandom_classfr 0 0.346
## Max.cor.Y.rcv.1X1###glmnet 0 0.907
## Max.cor.Y##rcv#rpart 5 1.862
## Interact.High.cor.Y##rcv#glmnet 25 4.406
## Low.cor.X##rcv#glmnet 25 19.107
## All.X##rcv#glmnet 25 19.069
## All.X##rcv#glm 1 11.096
## min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr 0.003 0.5000000
## Random###myrandom_classfr 0.002 0.4942483
## Max.cor.Y.rcv.1X1###glmnet 0.064 0.5971118
## Max.cor.Y##rcv#rpart 0.019 0.5971118
## Interact.High.cor.Y##rcv#glmnet 0.278 0.6115819
## Low.cor.X##rcv#glmnet 1.869 0.6451920
## All.X##rcv#glmnet 1.889 0.6451920
## All.X##rcv#glm 1.313 0.6722741
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.4619799 0.5265168 0.5073101
## Max.cor.Y.rcv.1X1###glmnet 0.5480631 0.6461604 0.3580613
## Max.cor.Y##rcv#rpart 0.5480631 0.6461604 0.3676308
## Interact.High.cor.Y##rcv#glmnet 0.5939742 0.6291896 0.3369481
## Low.cor.X##rcv#glmnet 0.5805835 0.7098006 0.2855263
## All.X##rcv#glmnet 0.5805835 0.7098006 0.2855263
## All.X##rcv#glm 0.6432329 0.7013152 0.2700242
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.5 0.6395473
## Random###myrandom_classfr 0.6 0.6395473
## Max.cor.Y.rcv.1X1###glmnet 0.6 0.6720662
## Max.cor.Y##rcv#rpart 0.6 0.6720662
## Interact.High.cor.Y##rcv#glmnet 0.6 0.6723830
## Low.cor.X##rcv#glmnet 0.6 0.6835023
## All.X##rcv#glmnet 0.6 0.6835023
## All.X##rcv#glm 0.6 0.6865016
## max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr 0.4700989 0.4553427
## Random###myrandom_classfr 0.4700989 0.4553427
## Max.cor.Y.rcv.1X1###glmnet 0.5721673 0.5574714
## Max.cor.Y##rcv#rpart 0.6000450 0.5574714
## Interact.High.cor.Y##rcv#glmnet 0.5996718 0.5730476
## Low.cor.X##rcv#glmnet 0.6218534 0.5994934
## All.X##rcv#glmnet 0.6218534 0.5994934
## All.X##rcv#glm 0.5999708 0.6327828
## max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr 0.4848945 0.0000000
## Random###myrandom_classfr 0.4848945 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.5867683 0.1772539
## Max.cor.Y##rcv#rpart 0.5867683 0.1947896
## Interact.High.cor.Y##rcv#glmnet 0.6021969 0.1976596
## Low.cor.X##rcv#glmnet 0.6283244 0.2372918
## All.X##rcv#glmnet 0.6283244 0.2372918
## All.X##rcv#glm 0.6610886 0.1961948
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 0.0000000 1.0000000
## Random###myrandom_classfr 0.5235690 0.5000000 0.5471380
## Max.cor.Y.rcv.1X1###glmnet 0.5896897 0.5228137 0.6565657
## Max.cor.Y##rcv#rpart 0.5896897 0.5228137 0.6565657
## Interact.High.cor.Y##rcv#glmnet 0.5920549 0.5494297 0.6346801
## Low.cor.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glm 0.5998195 0.5380228 0.6616162
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.5
## Random###myrandom_classfr 0.5191202 0.6
## Max.cor.Y.rcv.1X1###glmnet 0.3658672 0.6
## Max.cor.Y##rcv#rpart 0.3774772 0.6
## Interact.High.cor.Y##rcv#glmnet 0.3664097 0.7
## Low.cor.X##rcv#glmnet 0.3187291 0.7
## All.X##rcv#glmnet 0.3187291 0.7
## All.X##rcv#glm 0.3425094 0.7
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.6391252 0.4696429
## Random###myrandom_classfr 0.6391252 0.4696429
## Max.cor.Y.rcv.1X1###glmnet 0.6643789 0.5633929
## Max.cor.Y##rcv#rpart 0.6643789 0.5633929
## Interact.High.cor.Y##rcv#glmnet 0.6657572 0.5625000
## Low.cor.X##rcv#glmnet 0.6648721 0.5553571
## All.X##rcv#glmnet 0.6648721 0.5553571
## All.X##rcv#glm 0.6627991 0.5848214
## max.AccuracyLower.OOB
## MFO###myMFO_classfr 0.4400805
## Random###myrandom_classfr 0.4400805
## Max.cor.Y.rcv.1X1###glmnet 0.5337655
## Max.cor.Y##rcv#rpart 0.5337655
## Interact.High.cor.Y##rcv#glmnet 0.5328684
## Low.cor.X##rcv#glmnet 0.5256948
## All.X##rcv#glmnet 0.5256948
## All.X##rcv#glm 0.5553253
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr 0.4993651 0.0000000
## Random###myrandom_classfr 0.4993651 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.5926864 0.1605510
## Max.cor.Y##rcv#rpart 0.5926864 0.1605510
## Interact.High.cor.Y##rcv#glmnet 0.5918025 0.1596227
## Low.cor.X##rcv#glmnet 0.5847280 0.1476773
## All.X##rcv#glmnet 0.5847280 0.1476773
## All.X##rcv#glm 0.6138708 0.1952083
## max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr NA NA
## Random###myrandom_classfr NA NA
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.012403504 0.02559319
## Interact.High.cor.Y##rcv#glmnet 0.015267709 0.03220586
## Low.cor.X##rcv#glmnet 0.004944753 0.01073770
## All.X##rcv#glmnet 0.004944753 0.01073770
## All.X##rcv#glm 0.006222678 0.01300276rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 422.432 422.503
## 6 fit.models_1_end 1 5 teardown 422.503 NA
## elapsed
## 5 0.071
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 1 1 368.632 422.512 53.88
## 18 fit.models 8 2 2 422.512 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 426.412 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr 0 0.5000000
## Random###myrandom_classfr 0 0.4942483
## Max.cor.Y.rcv.1X1###glmnet 0 0.5971118
## Max.cor.Y##rcv#rpart 5 0.5971118
## Interact.High.cor.Y##rcv#glmnet 25 0.6115819
## Low.cor.X##rcv#glmnet 25 0.6451920
## All.X##rcv#glmnet 25 0.6451920
## All.X##rcv#glm 1 0.6722741
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.4619799 0.5265168 0.5073101
## Max.cor.Y.rcv.1X1###glmnet 0.5480631 0.6461604 0.3580613
## Max.cor.Y##rcv#rpart 0.5480631 0.6461604 0.3676308
## Interact.High.cor.Y##rcv#glmnet 0.5939742 0.6291896 0.3369481
## Low.cor.X##rcv#glmnet 0.5805835 0.7098006 0.2855263
## All.X##rcv#glmnet 0.5805835 0.7098006 0.2855263
## All.X##rcv#glm 0.6432329 0.7013152 0.2700242
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.5 0.6395473
## Random###myrandom_classfr 0.6 0.6395473
## Max.cor.Y.rcv.1X1###glmnet 0.6 0.6720662
## Max.cor.Y##rcv#rpart 0.6 0.6720662
## Interact.High.cor.Y##rcv#glmnet 0.6 0.6723830
## Low.cor.X##rcv#glmnet 0.6 0.6835023
## All.X##rcv#glmnet 0.6 0.6835023
## All.X##rcv#glm 0.6 0.6865016
## max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr 0.4700989 0.0000000
## Random###myrandom_classfr 0.4700989 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.5721673 0.1772539
## Max.cor.Y##rcv#rpart 0.6000450 0.1947896
## Interact.High.cor.Y##rcv#glmnet 0.5996718 0.1976596
## Low.cor.X##rcv#glmnet 0.6218534 0.2372918
## All.X##rcv#glmnet 0.6218534 0.2372918
## All.X##rcv#glm 0.5999708 0.1961948
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 0.0000000 1.0000000
## Random###myrandom_classfr 0.5235690 0.5000000 0.5471380
## Max.cor.Y.rcv.1X1###glmnet 0.5896897 0.5228137 0.6565657
## Max.cor.Y##rcv#rpart 0.5896897 0.5228137 0.6565657
## Interact.High.cor.Y##rcv#glmnet 0.5920549 0.5494297 0.6346801
## Low.cor.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glm 0.5998195 0.5380228 0.6616162
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.5
## Random###myrandom_classfr 0.5191202 0.6
## Max.cor.Y.rcv.1X1###glmnet 0.3658672 0.6
## Max.cor.Y##rcv#rpart 0.3774772 0.6
## Interact.High.cor.Y##rcv#glmnet 0.3664097 0.7
## Low.cor.X##rcv#glmnet 0.3187291 0.7
## All.X##rcv#glmnet 0.3187291 0.7
## All.X##rcv#glm 0.3425094 0.7
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.6391252 0.4696429
## Random###myrandom_classfr 0.6391252 0.4696429
## Max.cor.Y.rcv.1X1###glmnet 0.6643789 0.5633929
## Max.cor.Y##rcv#rpart 0.6643789 0.5633929
## Interact.High.cor.Y##rcv#glmnet 0.6657572 0.5625000
## Low.cor.X##rcv#glmnet 0.6648721 0.5553571
## All.X##rcv#glmnet 0.6648721 0.5553571
## All.X##rcv#glm 0.6627991 0.5848214
## max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr 0.0000000 2.33100233
## Random###myrandom_classfr 0.0000000 2.89017341
## Max.cor.Y.rcv.1X1###glmnet 0.1605510 1.10253583
## Max.cor.Y##rcv#rpart 0.1605510 0.53705693
## Interact.High.cor.Y##rcv#glmnet 0.1596227 0.22696323
## Low.cor.X##rcv#glmnet 0.1476773 0.05233684
## All.X##rcv#glmnet 0.1476773 0.05244113
## All.X##rcv#glm 0.1952083 0.09012257
## inv.elapsedtime.final
## MFO###myMFO_classfr 333.3333333
## Random###myrandom_classfr 500.0000000
## Max.cor.Y.rcv.1X1###glmnet 15.6250000
## Max.cor.Y##rcv#rpart 52.6315789
## Interact.High.cor.Y##rcv#glmnet 3.5971223
## Low.cor.X##rcv#glmnet 0.5350455
## All.X##rcv#glmnet 0.5293806
## All.X##rcv#glm 0.7616146# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id max.Accuracy.OOB max.AUCROCR.OOB
## 8 All.X##rcv#glm 0.5848214 0.3425094
## 4 Max.cor.Y##rcv#rpart 0.5633929 0.3774772
## 3 Max.cor.Y.rcv.1X1###glmnet 0.5633929 0.3658672
## 5 Interact.High.cor.Y##rcv#glmnet 0.5625000 0.3664097
## 6 Low.cor.X##rcv#glmnet 0.5553571 0.3187291
## 7 All.X##rcv#glmnet 0.5553571 0.3187291
## 2 Random###myrandom_classfr 0.4696429 0.5191202
## 1 MFO###myMFO_classfr 0.4696429 0.5000000
## max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 8 0.5998195 0.5999708 0.6
## 4 0.5896897 0.6000450 0.6
## 3 0.5896897 0.5721673 0.6
## 5 0.5920549 0.5996718 0.6
## 6 0.6261026 0.6218534 0.6
## 7 0.6261026 0.6218534 0.6
## 2 0.5235690 0.4700989 0.6
## 1 0.5000000 0.4700989 0.5
## opt.prob.threshold.OOB
## 8 0.7
## 4 0.6
## 3 0.6
## 5 0.7
## 6 0.7
## 7 0.7
## 2 0.6
## 1 0.5# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7fb10c14a4b0>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: All.X##rcv#glm"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glbMdlSelId))
glbMdlSelId <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glbMdlSelId)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])
## Length Class Mode
## a0 88 -none- numeric
## beta 18656 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 212 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.1952047024 -0.1067097341
## Edn.fctr^6 Edn.fctr^7
## 0.0237553435 0.0749098536
## Gender.fctrM Hhold.fctrMKy
## -0.0703554818 -0.1490438784
## Hhold.fctrPKn Hhold.fctrSKn
## 0.5162299703 0.0279618972
## Hhold.fctrSKy Income.fctr.Q
## 0.1041010847 -0.0675479898
## Income.fctr.C Income.fctr^4
## -0.1206129633 -0.0139722858
## Income.fctr^6 Q100010.fctrNo
## 0.0007504834 0.0299022728
## Q100680.fctrYes Q100689.fctrYes
## 0.0032625549 0.1031391064
## Q101163.fctrDad Q101163.fctrMom
## -0.0883139744 0.1099784036
## Q102687.fctrYes Q103293.fctrYes
## 0.0312745394 0.0034287002
## Q104996.fctrNo Q104996.fctrYes
## -0.0237434125 0.0270717473
## Q105655.fctrYes Q106042.fctrNo
## -0.0412155561 -0.0205425586
## Q106272.fctrNo Q106272.fctrYes
## 0.0175158157 -0.0284921306
## Q106389.fctrNo Q106997.fctrGrrr people
## -0.0748652171 -0.0247097710
## Q106997.fctrYay people! Q107491.fctrYes
## 0.0764310291 0.0241770403
## Q108342.fctrOnline Q108855.fctrYes!
## 0.0659699015 -0.0544186322
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.0399793905 -0.3612442626
## Q109244.fctrYes Q110740.fctrMac
## 0.7867720593 0.0220394463
## Q110740.fctrPC Q111220.fctrYes
## -0.0875939927 0.0991012112
## Q111848.fctrYes Q112270.fctrYes
## 0.0228026313 0.0045878812
## Q112478.fctrNo Q113181.fctrNo
## -0.0523849496 0.1851568513
## Q113181.fctrYes Q113992.fctrYes
## -0.1950370446 0.0126219886
## Q114386.fctrMysterious Q115195.fctrYes
## 0.0019680292 0.0030704916
## Q115390.fctrNo Q115390.fctrYes
## -0.0745460116 0.0200284127
## Q115611.fctrNo Q115611.fctrYes
## 0.1339408085 -0.3306757390
## Q115899.fctrCircumstances Q115899.fctrMe
## 0.0851135127 -0.0130795183
## Q116197.fctrA.M. Q116881.fctrHappy
## -0.0262309208 0.0777873678
## Q116881.fctrRight Q116953.fctrNo
## -0.1304209856 -0.0263705676
## Q116953.fctrYes Q117186.fctrHot headed
## 0.0533044251 -0.0115004864
## Q118232.fctrIdealist Q118233.fctrNo
## 0.1031325558 -0.0082146917
## Q118233.fctrYes Q119650.fctrGiving
## 0.0137212711 -0.0170457793
## Q119851.fctrNo Q119851.fctrYes
## -0.1077857388 0.0180092566
## Q120012.fctrYes Q120014.fctrNo
## 0.0366851197 0.0336193579
## Q120014.fctrYes Q120194.fctrStudy first
## -0.0283086949 0.0593355542
## Q120379.fctrNo Q120379.fctrYes
## -0.0455168710 0.1013771165
## Q120472.fctrScience Q120650.fctrYes
## -0.0264043163 -0.0258801338
## Q121699.fctrNo Q121699.fctrYes
## -0.0654967987 0.0477679578
## Q121700.fctrNo Q121700.fctrYes
## -0.0073966532 0.0193609196
## Q122120.fctrYes Q122771.fctrPt
## -0.0342637586 -0.1211971800
## Q123464.fctrNo Q124122.fctrNo
## -0.0135208179 -0.0227693557
## Q124742.fctrNo YOB.Age.fctr.L
## 0.0271760998 0.1178489426
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.0091672083 0.0423673496
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.0067997116 -0.0389680361
## YOB.Age.fctr^8
## -0.0633534034
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.192887820 -0.119849973
## Edn.fctr^6 Edn.fctr^7
## 0.029850747 0.081133497
## Gender.fctrM Hhold.fctrMKy
## -0.070364216 -0.151860222
## Hhold.fctrPKn Hhold.fctrSKn
## 0.534578849 0.033885621
## Hhold.fctrSKy Income.fctr.Q
## 0.114752162 -0.071829866
## Income.fctr.C Income.fctr^4
## -0.130161899 -0.019455874
## Income.fctr^6 Q100010.fctrNo
## 0.006025901 0.036436326
## Q100680.fctrYes Q100689.fctrYes
## 0.004776233 0.110778663
## Q101163.fctrDad Q101163.fctrMom
## -0.093942855 0.110568655
## Q102687.fctrYes Q103293.fctrYes
## 0.035917303 0.009187328
## Q104996.fctrNo Q104996.fctrYes
## -0.027174935 0.030840441
## Q105655.fctrYes Q106042.fctrNo
## -0.047754402 -0.022572851
## Q106272.fctrNo Q106272.fctrYes
## 0.018750136 -0.033094547
## Q106389.fctrNo Q106997.fctrGrrr people
## -0.081395435 -0.028816760
## Q106997.fctrYay people! Q107491.fctrYes
## 0.081972579 0.029114800
## Q107869.fctrNo Q108342.fctrOnline
## 0.002204830 0.070638186
## Q108855.fctrYes! Q108950.fctrRisk-friendly
## -0.060370840 0.045240840
## Q109244.fctrNo Q109244.fctrYes
## -0.368068961 0.798909343
## Q110740.fctrMac Q110740.fctrPC
## 0.022194873 -0.094040223
## Q111220.fctrYes Q111848.fctrYes
## 0.105711882 0.026423743
## Q112270.fctrYes Q112478.fctrNo
## 0.011178188 -0.058869716
## Q113181.fctrNo Q113181.fctrYes
## 0.188992531 -0.200672215
## Q113992.fctrYes Q114386.fctrMysterious
## 0.018408206 0.008787278
## Q115195.fctrYes Q115390.fctrNo
## 0.006667928 -0.080608044
## Q115390.fctrYes Q115611.fctrNo
## 0.020849025 0.133196312
## Q115611.fctrYes Q115899.fctrCircumstances
## -0.339016722 0.089628166
## Q115899.fctrMe Q116197.fctrA.M.
## -0.014103408 -0.033977326
## Q116881.fctrHappy Q116881.fctrRight
## 0.081808928 -0.134553463
## Q116953.fctrNo Q116953.fctrYes
## -0.029073266 0.059821703
## Q117186.fctrHot headed Q118232.fctrIdealist
## -0.017114236 0.109817378
## Q118233.fctrNo Q118233.fctrYes
## -0.012865284 0.016738520
## Q119650.fctrGiving Q119851.fctrNo
## -0.022919294 -0.111428922
## Q119851.fctrYes Q120012.fctrYes
## 0.019736959 0.041230245
## Q120014.fctrNo Q120014.fctrYes
## 0.037876336 -0.030635600
## Q120194.fctrStudy first Q120379.fctrNo
## 0.064690194 -0.045827828
## Q120379.fctrYes Q120472.fctrScience
## 0.108653701 -0.028048835
## Q120650.fctrYes Q121699.fctrNo
## -0.031043954 -0.063199374
## Q121699.fctrYes Q121700.fctrNo
## 0.055354400 -0.011608644
## Q121700.fctrYes Q122120.fctrYes
## 0.019772249 -0.039779331
## Q122771.fctrPt Q123464.fctrNo
## -0.129074066 -0.018364756
## Q124122.fctrNo Q124122.fctrYes
## -0.026727216 0.001702308
## Q124742.fctrNo YOB.Age.fctr.L
## 0.034842422 0.133354527
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.022697089 0.051333522
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.014400447 -0.046938593
## YOB.Age.fctr^8
## -0.071194408## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp
## Q109244.fctrYes 100.0000000 100.0000000
## Hhold.fctrPKn 66.6656452 66.6656452
## Q109244.fctrNo 46.0415425 46.0415425
## Q115611.fctrYes 42.3575968 42.3575968
## Q113181.fctrYes 25.0555685 25.0555685
## Q113181.fctrNo 23.6329372 23.6329372
## Hhold.fctrMKy 18.9960973 18.9960973
## Q116881.fctrRight 16.7915189 16.7915189
## Q115611.fctrNo 16.7393728 16.7393728
## YOB.Age.fctr.L 16.3652951 16.3652951
## Income.fctr.C 16.1088990 16.1088990
## Q122771.fctrPt 16.0128680 16.0128680
## Edn.fctr^4 14.7272891 14.7272891
## Hhold.fctrSKy 14.1476584 14.1476584
## Q119851.fctrNo 13.9003500 13.9003500
## Q101163.fctrMom 13.8663632 13.8663632
## Q100689.fctrYes 13.7218358 13.7218358
## Q118232.fctrIdealist 13.6243021 13.6243021
## Q120379.fctrYes 13.4638696 13.4638696
## Q111220.fctrYes 13.1107000 13.1107000
## Q101163.fctrDad 11.6570297 11.6570297
## Q110740.fctrPC 11.6494388 11.6494388
## Q115899.fctrCircumstances 11.1423796 11.1423796
## Q116881.fctrHappy 10.1727145 10.1727145
## Q106997.fctrYay people! 10.1564110 10.1564110
## Q106389.fctrNo 10.0599893 10.0599893
## Edn.fctr^7 10.0345380 10.0345380
## Q115390.fctrNo 9.9724910 9.9724910
## Income.fctr.Q 8.9136429 8.9136429
## Gender.fctrM 8.8332377 8.8332377
## Q108342.fctrOnline 8.7546733 8.7546733
## YOB.Age.fctr^8 8.7475866 8.7475866
## Q120194.fctrStudy first 7.9913298 7.9913298
## Q121699.fctrNo 7.9896777 7.9896777
## Q108855.fctrYes! 7.4345962 7.4345962
## Q116953.fctrYes 7.3519595 7.3519595
## Q112478.fctrNo 7.2332361 7.2332361
## Q121699.fctrYes 6.7652199 6.7652199
## YOB.Age.fctr^4 6.2269952 6.2269952
## Q105655.fctrYes 5.8365204 5.8365204
## Q120379.fctrNo 5.7456388 5.7456388
## YOB.Age.fctr^7 5.6993965 5.6993965
## Q108950.fctrRisk-friendly 5.5519375 5.5519375
## Q120012.fctrYes 5.0658170 5.0658170
## Q122120.fctrYes 4.8601449 4.8601449
## Q120014.fctrNo 4.6517561 4.6517561
## Q100010.fctrNo 4.4157772 4.4157772
## Q102687.fctrYes 4.3964689 4.3964689
## Q124742.fctrNo 4.1882314 4.1882314
## Hhold.fctrSKn 4.1103602 4.1103602
## Q116197.fctrA.M. 4.0776867 4.0776867
## Q106272.fctrYes 4.0430812 4.0430812
## Q120014.fctrYes 3.7895513 3.7895513
## Q104996.fctrYes 3.7803145 3.7803145
## Q120650.fctrYes 3.7720422 3.7720422
## Edn.fctr^6 3.5996645 3.5996645
## Q116953.fctrNo 3.5843074 3.5843074
## Q107491.fctrYes 3.5353385 3.5353385
## Q106997.fctrGrrr people 3.5180627 3.5180627
## Q120472.fctrScience 3.4813542 3.4813542
## Q104996.fctrNo 3.3283245 3.3283245
## Q124122.fctrNo 3.2593588 3.2593588
## Q111848.fctrYes 3.2294247 3.2294247
## Q106042.fctrNo 2.7845529 2.7845529
## Q110740.fctrMac 2.7825530 2.7825530
## Q119650.fctrGiving 2.7348762 2.7348762
## Q115390.fctrYes 2.5974711 2.5974711
## YOB.Age.fctr.Q 2.5213728 2.5213728
## Q121700.fctrYes 2.4722157 2.4722157
## Q119851.fctrYes 2.4358735 2.4358735
## Q106272.fctrNo 2.3239495 2.3239495
## Income.fctr^4 2.3095349 2.3095349
## Q123464.fctrNo 2.1880638 2.1880638
## Q113992.fctrYes 2.1706753 2.1706753
## Q118233.fctrYes 2.0281910 2.0281910
## Q117186.fctrHot headed 2.0124127 2.0124127
## Q115899.fctrMe 1.7457055 1.7457055
## YOB.Age.fctr^6 1.6235574 1.6235574
## Q118233.fctrNo 1.5023529 1.5023529
## Q121700.fctrNo 1.3552284 1.3552284
## Q112270.fctrYes 1.2435335 1.2435335
## Q103293.fctrYes 1.0137649 1.0137649
## Q114386.fctrMysterious 0.9378311 0.9378311
## Q115195.fctrYes 0.7498744 0.7498744
## Income.fctr^6 0.6285968 0.6285968
## Q100680.fctrYes 0.5629082 0.5629082
## Q107869.fctrNo 0.2233418 0.2233418
## Q124122.fctrYes 0.1724380 0.1724380
## .rnorm 0.0000000 0.0000000
## Edn.fctr.L 0.0000000 0.0000000
## Edn.fctr.Q 0.0000000 0.0000000
## Edn.fctr.C 0.0000000 0.0000000
## Edn.fctr^5 0.0000000 0.0000000
## Gender.fctrF 0.0000000 0.0000000
## Hhold.fctrMKn 0.0000000 0.0000000
## Hhold.fctrPKy 0.0000000 0.0000000
## Income.fctr.L 0.0000000 0.0000000
## Income.fctr^5 0.0000000 0.0000000
## Q100010.fctrYes 0.0000000 0.0000000
## Q100562.fctrNo 0.0000000 0.0000000
## Q100562.fctrYes 0.0000000 0.0000000
## Q100680.fctrNo 0.0000000 0.0000000
## Q100689.fctrNo 0.0000000 0.0000000
## Q101162.fctrOptimist 0.0000000 0.0000000
## Q101162.fctrPessimist 0.0000000 0.0000000
## Q101596.fctrNo 0.0000000 0.0000000
## Q101596.fctrYes 0.0000000 0.0000000
## Q102089.fctrOwn 0.0000000 0.0000000
## Q102089.fctrRent 0.0000000 0.0000000
## Q102289.fctrNo 0.0000000 0.0000000
## Q102289.fctrYes 0.0000000 0.0000000
## Q102674.fctrNo 0.0000000 0.0000000
## Q102674.fctrYes 0.0000000 0.0000000
## Q102687.fctrNo 0.0000000 0.0000000
## Q102906.fctrNo 0.0000000 0.0000000
## Q102906.fctrYes 0.0000000 0.0000000
## Q103293.fctrNo 0.0000000 0.0000000
## Q105655.fctrNo 0.0000000 0.0000000
## Q105840.fctrNo 0.0000000 0.0000000
## Q105840.fctrYes 0.0000000 0.0000000
## Q106042.fctrYes 0.0000000 0.0000000
## Q106388.fctrNo 0.0000000 0.0000000
## Q106388.fctrYes 0.0000000 0.0000000
## Q106389.fctrYes 0.0000000 0.0000000
## Q106993.fctrNo 0.0000000 0.0000000
## Q106993.fctrYes 0.0000000 0.0000000
## Q107491.fctrNo 0.0000000 0.0000000
## Q107869.fctrYes 0.0000000 0.0000000
## Q108342.fctrIn-person 0.0000000 0.0000000
## Q108343.fctrNo 0.0000000 0.0000000
## Q108343.fctrYes 0.0000000 0.0000000
## Q108617.fctrNo 0.0000000 0.0000000
## Q108617.fctrYes 0.0000000 0.0000000
## Q108754.fctrNo 0.0000000 0.0000000
## Q108754.fctrYes 0.0000000 0.0000000
## Q108855.fctrUmm... 0.0000000 0.0000000
## Q108856.fctrSocialize 0.0000000 0.0000000
## Q108856.fctrSpace 0.0000000 0.0000000
## Q108950.fctrCautious 0.0000000 0.0000000
## Q109367.fctrNo 0.0000000 0.0000000
## Q109367.fctrYes 0.0000000 0.0000000
## Q111220.fctrNo 0.0000000 0.0000000
## Q111580.fctrDemanding 0.0000000 0.0000000
## Q111580.fctrSupportive 0.0000000 0.0000000
## Q111848.fctrNo 0.0000000 0.0000000
## Q112270.fctrNo 0.0000000 0.0000000
## Q112478.fctrYes 0.0000000 0.0000000
## Q112512.fctrNo 0.0000000 0.0000000
## Q112512.fctrYes 0.0000000 0.0000000
## Q113583.fctrTalk 0.0000000 0.0000000
## Q113583.fctrTunes 0.0000000 0.0000000
## Q113584.fctrPeople 0.0000000 0.0000000
## Q113584.fctrTechnology 0.0000000 0.0000000
## Q113992.fctrNo 0.0000000 0.0000000
## Q114152.fctrNo 0.0000000 0.0000000
## Q114152.fctrYes 0.0000000 0.0000000
## Q114386.fctrTMI 0.0000000 0.0000000
## Q114517.fctrNo 0.0000000 0.0000000
## Q114517.fctrYes 0.0000000 0.0000000
## Q114748.fctrNo 0.0000000 0.0000000
## Q114748.fctrYes 0.0000000 0.0000000
## Q114961.fctrNo 0.0000000 0.0000000
## Q114961.fctrYes 0.0000000 0.0000000
## Q115195.fctrNo 0.0000000 0.0000000
## Q115602.fctrNo 0.0000000 0.0000000
## Q115602.fctrYes 0.0000000 0.0000000
## Q115610.fctrNo 0.0000000 0.0000000
## Q115610.fctrYes 0.0000000 0.0000000
## Q115777.fctrEnd 0.0000000 0.0000000
## Q115777.fctrStart 0.0000000 0.0000000
## Q116197.fctrP.M. 0.0000000 0.0000000
## Q116441.fctrNo 0.0000000 0.0000000
## Q116441.fctrYes 0.0000000 0.0000000
## Q116448.fctrNo 0.0000000 0.0000000
## Q116448.fctrYes 0.0000000 0.0000000
## Q116601.fctrNo 0.0000000 0.0000000
## Q116601.fctrYes 0.0000000 0.0000000
## Q116797.fctrNo 0.0000000 0.0000000
## Q116797.fctrYes 0.0000000 0.0000000
## Q117186.fctrCool headed 0.0000000 0.0000000
## Q117193.fctrOdd hours 0.0000000 0.0000000
## Q117193.fctrStandard hours 0.0000000 0.0000000
## Q118117.fctrNo 0.0000000 0.0000000
## Q118117.fctrYes 0.0000000 0.0000000
## Q118232.fctrPragmatist 0.0000000 0.0000000
## Q118237.fctrNo 0.0000000 0.0000000
## Q118237.fctrYes 0.0000000 0.0000000
## Q118892.fctrNo 0.0000000 0.0000000
## Q118892.fctrYes 0.0000000 0.0000000
## Q119334.fctrNo 0.0000000 0.0000000
## Q119334.fctrYes 0.0000000 0.0000000
## Q119650.fctrReceiving 0.0000000 0.0000000
## Q120012.fctrNo 0.0000000 0.0000000
## Q120194.fctrTry first 0.0000000 0.0000000
## Q120472.fctrArt 0.0000000 0.0000000
## Q120650.fctrNo 0.0000000 0.0000000
## Q120978.fctrNo 0.0000000 0.0000000
## Q120978.fctrYes 0.0000000 0.0000000
## Q121011.fctrNo 0.0000000 0.0000000
## Q121011.fctrYes 0.0000000 0.0000000
## Q122120.fctrNo 0.0000000 0.0000000
## Q122769.fctrNo 0.0000000 0.0000000
## Q122769.fctrYes 0.0000000 0.0000000
## Q122770.fctrNo 0.0000000 0.0000000
## Q122770.fctrYes 0.0000000 0.0000000
## Q122771.fctrPc 0.0000000 0.0000000
## Q123464.fctrYes 0.0000000 0.0000000
## Q123621.fctrNo 0.0000000 0.0000000
## Q123621.fctrYes 0.0000000 0.0000000
## Q124742.fctrYes 0.0000000 0.0000000
## YOB.Age.fctr.C 0.0000000 0.0000000
## YOB.Age.fctr^5 0.0000000 0.0000000#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 97
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1187 D 0.2294075
## 2 2798 D 0.2478459
## 3 1393 D 0.2620899
## 4 943 D 0.2629215
## 5 1843 D 0.2678920
## 6 1045 D 0.2691518
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.7705925 FALSE
## 2 0.7521541 FALSE
## 3 0.7379101 FALSE
## 4 0.7370785 FALSE
## 5 0.7321080 FALSE
## 6 0.7308482 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.4705925
## 2 FALSE -0.4521541
## 3 FALSE -0.4379101
## 4 FALSE -0.4370785
## 5 FALSE -0.4321080
## 6 FALSE -0.4308482
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 12 697 D 0.2982021
## 105 1261 D 0.4524387
## 205 6755 D 0.5220793
## 250 1411 D 0.5416549
## 306 901 D 0.5632237
## 448 3821 D 0.6716883
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 12 R TRUE
## 105 R TRUE
## 205 R TRUE
## 250 R TRUE
## 306 R TRUE
## 448 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 12 0.7017979
## 105 0.5475613
## 205 0.4779207
## 250 0.4583451
## 306 0.4367763
## 448 0.3283117
## Party.fctr.All.X..rcv.glmnet.is.acc
## 12 FALSE
## 105 FALSE
## 205 FALSE
## 250 FALSE
## 306 FALSE
## 448 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 12 FALSE
## 105 FALSE
## 205 FALSE
## 250 FALSE
## 306 FALSE
## 448 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 12 -0.40179786
## 105 -0.24756127
## 205 -0.17792067
## 250 -0.15834505
## 306 -0.13677628
## 448 -0.02831165
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 493 520 R 0.8285645
## 494 5466 R 0.8350278
## 495 2957 R 0.8384858
## 496 5148 R 0.8428523
## 497 1307 R 0.8473741
## 498 451 R 0.8712485
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 493 D TRUE
## 494 D TRUE
## 495 D TRUE
## 496 D TRUE
## 497 D TRUE
## 498 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 493 0.8285645
## 494 0.8350278
## 495 0.8384858
## 496 0.8428523
## 497 0.8473741
## 498 0.8712485
## Party.fctr.All.X..rcv.glmnet.is.acc
## 493 FALSE
## 494 FALSE
## 495 FALSE
## 496 FALSE
## 497 FALSE
## 498 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 493 FALSE
## 494 FALSE
## 495 FALSE
## 496 FALSE
## 497 FALSE
## 498 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 493 0.1285645
## 494 0.1350278
## 495 0.1384858
## 496 0.1428523
## 497 0.1473741
## 498 0.1712485
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy PKy 9 52 10 0.01169065 0.008035714
## PKn PKn 30 150 37 0.03372302 0.026785714
## N N 83 367 102 0.08250899 0.074107143
## SKn SKn 511 1920 638 0.43165468 0.456250000
## MKn MKn 136 516 169 0.11600719 0.121428571
## SKy SKy 53 147 65 0.03304856 0.047321429
## MKy MKy 298 1296 371 0.29136691 0.266071429
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy 0.007183908 24.52672 0.4716676 52 4.439736
## PKn 0.026580460 54.07667 0.3605111 150 14.275241
## N 0.073275862 170.90149 0.4656716 367 37.875827
## SKn 0.458333333 861.17518 0.4485287 1920 232.827797
## MKn 0.121408046 230.56787 0.4468370 516 61.957461
## SKy 0.046695402 62.64058 0.4261264 147 23.920386
## MKy 0.266522989 575.87008 0.4443442 1296 132.600733
## err.abs.OOB.mean
## PKy 0.4933040
## PKn 0.4758414
## N 0.4563353
## SKn 0.4556317
## MKn 0.4555696
## SKy 0.4513280
## MKy 0.4449689
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1120.000000 4448.000000 1392.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 1979.758590 3.063687
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 4448.000000 507.897181 3.232979write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 434.365 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 18 fit.models 8 2 2 422.512 434.376 11.864
## 19 fit.models 8 3 3 434.376 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 19 fit.models 8 3 3 434.376 438.901
## 20 fit.data.training 9 0 0 438.901 NA
## elapsed
## 19 4.525
## 20 NAStep 9.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glbMdlSelId")
glbMdlFinId <- paste0("Final.", glbMdlSelId)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
mdlDf$id <- glbMdlFinId
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indepVar <- mygetIndepVar(glb_feats_df)
rfe_trn_results <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glbMdlSelId
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glbMdlSelId)) != -1))
ths_preProcess <- str_sub(glbMdlSelId, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
"Final.Ensemble", "Final")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.697000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0113 on full training set
## [1] "myfit_mdl: train complete: 23.170000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 78 -none- numeric
## beta 16536 dgCMatrix S4
## df 78 -none- numeric
## dim 2 -none- numeric
## lambda 78 -none- numeric
## dev.ratio 78 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 212 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.L
## 0.199317507 0.010575818
## Gender.fctrM Hhold.fctrMKy
## -0.089353036 -0.053644126
## Hhold.fctrPKn Income.fctr.Q
## 0.351733652 -0.027041500
## Income.fctr.C Q100689.fctrYes
## -0.019452829 0.064278964
## Q101163.fctrDad Q101163.fctrMom
## -0.030502858 0.100451334
## Q106997.fctrGrrr people Q108855.fctrYes!
## -0.011683852 -0.010941409
## Q109244.fctrNo Q109244.fctrYes
## -0.329607513 0.973614651
## Q110740.fctrPC Q113181.fctrNo
## -0.059059061 0.190175476
## Q113181.fctrYes Q115390.fctrYes
## -0.245302116 0.028506902
## Q115611.fctrNo Q115611.fctrYes
## 0.151059581 -0.317423919
## Q115899.fctrCircumstances Q116881.fctrRight
## 0.028498235 -0.147331307
## Q118232.fctrIdealist Q119851.fctrNo
## 0.061143167 -0.074663390
## Q120194.fctrStudy first Q120379.fctrYes
## 0.003760908 0.049220750
## Q120472.fctrScience Q121699.fctrYes
## -0.038845973 0.020085224
## Q122771.fctrPt YOB.Age.fctr.L
## -0.045014766 0.007388581
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.L
## 0.205373851 0.017722336
## Gender.fctrM Hhold.fctrMKy
## -0.091935724 -0.068703734
## Hhold.fctrPKn Income.fctr.Q
## 0.368529532 -0.036198758
## Income.fctr.C Q100689.fctrYes
## -0.035966293 0.074786011
## Q101163.fctrDad Q101163.fctrMom
## -0.037242891 0.105261638
## Q106042.fctrNo Q106389.fctrNo
## -0.002517763 -0.007645282
## Q106997.fctrGrrr people Q108855.fctrYes!
## -0.024295419 -0.021977986
## Q109244.fctrNo Q109244.fctrYes
## -0.331767116 0.978304078
## Q110740.fctrMac Q110740.fctrPC
## 0.006567562 -0.063438817
## Q112478.fctrNo Q113181.fctrNo
## -0.004436373 0.199435328
## Q113181.fctrYes Q115195.fctrYes
## -0.247560048 0.005146233
## Q115390.fctrYes Q115611.fctrNo
## 0.039119147 0.150595306
## Q115611.fctrYes Q115899.fctrCircumstances
## -0.327359553 0.038927387
## Q116881.fctrRight Q118232.fctrIdealist
## -0.158233644 0.072613253
## Q119851.fctrNo Q120194.fctrStudy first
## -0.085729511 0.016460971
## Q120379.fctrNo Q120379.fctrYes
## -0.002200789 0.059326120
## Q120472.fctrScience Q121699.fctrYes
## -0.049438382 0.029527323
## Q122120.fctrYes Q122771.fctrPt
## -0.005265105 -0.060124779
## YOB.Age.fctr.L YOB.Age.fctr^8
## 0.025718500 -0.005439800
## [1] "myfit_mdl: train diagnostics complete: 23.809000 secs"
## Prediction
## Reference R D
## R 2369 248
## D 2019 932
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.928520e-01 2.129065e-01 5.798116e-01 6.057950e-01 5.299928e-01
## AccuracyPValue McnemarPValue
## 2.244697e-21 1.749427e-302
## [1] "myfit_mdl: predict complete: 29.242000 secs"
## id
## 1 Final##rcv#glmnet
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 22.365 1.96
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6301535 0.5513947 0.7089122 0.3043179
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.676374 0.6290113
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5798116 0.605795 0.2511044
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01287164 0.02604359
## [1] "myfit_mdl: exit: 29.258000 secs"rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 20 fit.data.training 9 0 0 438.901 468.738
## 21 fit.data.training 9 1 1 468.738 NA
## elapsed
## 20 29.837
## 21 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.7glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes 100.0000000 100.0000000
## Hhold.fctrPKn 66.6656452 36.9944053
## Q109244.fctrNo 46.0415425 33.8868736
## Q115611.fctrYes 42.3575968 33.0857210
## Q113181.fctrYes 25.0555685 25.2568478
## Q113181.fctrNo 23.6329372 20.0124144
## Q116881.fctrRight 16.7915189 15.7181326
## Q115611.fctrNo 16.7393728 15.4468453
## Q101163.fctrMom 13.8663632 10.5659833
## Gender.fctrM 8.8332377 9.3011377
## Q119851.fctrNo 13.9003500 8.2839330
## Q100689.fctrYes 13.7218358 7.1880951
## Q118232.fctrIdealist 13.6243021 6.9222265
## Hhold.fctrMKy 18.9960973 6.3603486
## Q110740.fctrPC 11.6494388 6.3012963
## Q120379.fctrYes 13.4638696 5.6225507
## Q122771.fctrPt 16.0128680 5.4793117
## Q120472.fctrScience 3.4813542 4.5878163
## Q115390.fctrYes 2.5974711 3.5298914
## Q115899.fctrCircumstances 11.1423796 3.5184821
## Q101163.fctrDad 11.6570297 3.5118256
## Income.fctr.Q 8.9136429 3.2961742
## Income.fctr.C 16.1088990 2.9415690
## Q121699.fctrYes 6.7652199 2.5999887
## Q106997.fctrGrrr people 3.5180627 1.9215434
## YOB.Age.fctr.L 16.3652951 1.8101721
## Q108855.fctrYes! 7.4345962 1.7549852
## Edn.fctr.L 0.0000000 1.4939928
## Q120194.fctrStudy first 7.9913298 1.1150559
## Q106389.fctrNo 10.0599893 0.4393390
## Q110740.fctrMac 2.7825530 0.3774074
## YOB.Age.fctr^8 8.7475866 0.3126001
## Q122120.fctrYes 4.8601449 0.3025612
## Q115195.fctrYes 0.7498744 0.2957302
## Q112478.fctrNo 7.2332361 0.2549378
## Q106042.fctrNo 2.7845529 0.1446842
## Q120379.fctrNo 5.7456388 0.1264692
## .rnorm 0.0000000 0.0000000
## Edn.fctr.C 0.0000000 0.0000000
## Edn.fctr.Q 0.0000000 0.0000000
## Edn.fctr^4 14.7272891 0.0000000
## Edn.fctr^5 0.0000000 0.0000000
## Edn.fctr^6 3.5996645 0.0000000
## Edn.fctr^7 10.0345380 0.0000000
## Gender.fctrF 0.0000000 0.0000000
## Hhold.fctrMKn 0.0000000 0.0000000
## Hhold.fctrPKy 0.0000000 0.0000000
## Hhold.fctrSKn 4.1103602 0.0000000
## Hhold.fctrSKy 14.1476584 0.0000000
## Income.fctr.L 0.0000000 0.0000000
## Income.fctr^4 2.3095349 0.0000000
## Income.fctr^5 0.0000000 0.0000000
## Income.fctr^6 0.6285968 0.0000000
## Q100010.fctrNo 4.4157772 0.0000000
## Q100010.fctrYes 0.0000000 0.0000000
## Q100562.fctrNo 0.0000000 0.0000000
## Q100562.fctrYes 0.0000000 0.0000000
## Q100680.fctrNo 0.0000000 0.0000000
## Q100680.fctrYes 0.5629082 0.0000000
## Q100689.fctrNo 0.0000000 0.0000000
## Q101162.fctrOptimist 0.0000000 0.0000000
## Q101162.fctrPessimist 0.0000000 0.0000000
## Q101596.fctrNo 0.0000000 0.0000000
## Q101596.fctrYes 0.0000000 0.0000000
## Q102089.fctrOwn 0.0000000 0.0000000
## Q102089.fctrRent 0.0000000 0.0000000
## Q102289.fctrNo 0.0000000 0.0000000
## Q102289.fctrYes 0.0000000 0.0000000
## Q102674.fctrNo 0.0000000 0.0000000
## Q102674.fctrYes 0.0000000 0.0000000
## Q102687.fctrNo 0.0000000 0.0000000
## Q102687.fctrYes 4.3964689 0.0000000
## Q102906.fctrNo 0.0000000 0.0000000
## Q102906.fctrYes 0.0000000 0.0000000
## Q103293.fctrNo 0.0000000 0.0000000
## Q103293.fctrYes 1.0137649 0.0000000
## Q104996.fctrNo 3.3283245 0.0000000
## Q104996.fctrYes 3.7803145 0.0000000
## Q105655.fctrNo 0.0000000 0.0000000
## Q105655.fctrYes 5.8365204 0.0000000
## Q105840.fctrNo 0.0000000 0.0000000
## Q105840.fctrYes 0.0000000 0.0000000
## Q106042.fctrYes 0.0000000 0.0000000
## Q106272.fctrNo 2.3239495 0.0000000
## Q106272.fctrYes 4.0430812 0.0000000
## Q106388.fctrNo 0.0000000 0.0000000
## Q106388.fctrYes 0.0000000 0.0000000
## Q106389.fctrYes 0.0000000 0.0000000
## Q106993.fctrNo 0.0000000 0.0000000
## Q106993.fctrYes 0.0000000 0.0000000
## Q106997.fctrYay people! 10.1564110 0.0000000
## Q107491.fctrNo 0.0000000 0.0000000
## Q107491.fctrYes 3.5353385 0.0000000
## Q107869.fctrNo 0.2233418 0.0000000
## Q107869.fctrYes 0.0000000 0.0000000
## Q108342.fctrIn-person 0.0000000 0.0000000
## Q108342.fctrOnline 8.7546733 0.0000000
## Q108343.fctrNo 0.0000000 0.0000000
## Q108343.fctrYes 0.0000000 0.0000000
## Q108617.fctrNo 0.0000000 0.0000000
## Q108617.fctrYes 0.0000000 0.0000000
## Q108754.fctrNo 0.0000000 0.0000000
## Q108754.fctrYes 0.0000000 0.0000000
## Q108855.fctrUmm... 0.0000000 0.0000000
## Q108856.fctrSocialize 0.0000000 0.0000000
## Q108856.fctrSpace 0.0000000 0.0000000
## Q108950.fctrCautious 0.0000000 0.0000000
## Q108950.fctrRisk-friendly 5.5519375 0.0000000
## Q109367.fctrNo 0.0000000 0.0000000
## Q109367.fctrYes 0.0000000 0.0000000
## Q111220.fctrNo 0.0000000 0.0000000
## Q111220.fctrYes 13.1107000 0.0000000
## Q111580.fctrDemanding 0.0000000 0.0000000
## Q111580.fctrSupportive 0.0000000 0.0000000
## Q111848.fctrNo 0.0000000 0.0000000
## Q111848.fctrYes 3.2294247 0.0000000
## Q112270.fctrNo 0.0000000 0.0000000
## Q112270.fctrYes 1.2435335 0.0000000
## Q112478.fctrYes 0.0000000 0.0000000
## Q112512.fctrNo 0.0000000 0.0000000
## Q112512.fctrYes 0.0000000 0.0000000
## Q113583.fctrTalk 0.0000000 0.0000000
## Q113583.fctrTunes 0.0000000 0.0000000
## Q113584.fctrPeople 0.0000000 0.0000000
## Q113584.fctrTechnology 0.0000000 0.0000000
## Q113992.fctrNo 0.0000000 0.0000000
## Q113992.fctrYes 2.1706753 0.0000000
## Q114152.fctrNo 0.0000000 0.0000000
## Q114152.fctrYes 0.0000000 0.0000000
## Q114386.fctrMysterious 0.9378311 0.0000000
## Q114386.fctrTMI 0.0000000 0.0000000
## Q114517.fctrNo 0.0000000 0.0000000
## Q114517.fctrYes 0.0000000 0.0000000
## Q114748.fctrNo 0.0000000 0.0000000
## Q114748.fctrYes 0.0000000 0.0000000
## Q114961.fctrNo 0.0000000 0.0000000
## Q114961.fctrYes 0.0000000 0.0000000
## Q115195.fctrNo 0.0000000 0.0000000
## Q115390.fctrNo 9.9724910 0.0000000
## Q115602.fctrNo 0.0000000 0.0000000
## Q115602.fctrYes 0.0000000 0.0000000
## Q115610.fctrNo 0.0000000 0.0000000
## Q115610.fctrYes 0.0000000 0.0000000
## Q115777.fctrEnd 0.0000000 0.0000000
## Q115777.fctrStart 0.0000000 0.0000000
## Q115899.fctrMe 1.7457055 0.0000000
## Q116197.fctrA.M. 4.0776867 0.0000000
## Q116197.fctrP.M. 0.0000000 0.0000000
## Q116441.fctrNo 0.0000000 0.0000000
## Q116441.fctrYes 0.0000000 0.0000000
## Q116448.fctrNo 0.0000000 0.0000000
## Q116448.fctrYes 0.0000000 0.0000000
## Q116601.fctrNo 0.0000000 0.0000000
## Q116601.fctrYes 0.0000000 0.0000000
## Q116797.fctrNo 0.0000000 0.0000000
## Q116797.fctrYes 0.0000000 0.0000000
## Q116881.fctrHappy 10.1727145 0.0000000
## Q116953.fctrNo 3.5843074 0.0000000
## Q116953.fctrYes 7.3519595 0.0000000
## Q117186.fctrCool headed 0.0000000 0.0000000
## Q117186.fctrHot headed 2.0124127 0.0000000
## Q117193.fctrOdd hours 0.0000000 0.0000000
## Q117193.fctrStandard hours 0.0000000 0.0000000
## Q118117.fctrNo 0.0000000 0.0000000
## Q118117.fctrYes 0.0000000 0.0000000
## Q118232.fctrPragmatist 0.0000000 0.0000000
## Q118233.fctrNo 1.5023529 0.0000000
## Q118233.fctrYes 2.0281910 0.0000000
## Q118237.fctrNo 0.0000000 0.0000000
## Q118237.fctrYes 0.0000000 0.0000000
## Q118892.fctrNo 0.0000000 0.0000000
## Q118892.fctrYes 0.0000000 0.0000000
## Q119334.fctrNo 0.0000000 0.0000000
## Q119334.fctrYes 0.0000000 0.0000000
## Q119650.fctrGiving 2.7348762 0.0000000
## Q119650.fctrReceiving 0.0000000 0.0000000
## Q119851.fctrYes 2.4358735 0.0000000
## Q120012.fctrNo 0.0000000 0.0000000
## Q120012.fctrYes 5.0658170 0.0000000
## Q120014.fctrNo 4.6517561 0.0000000
## Q120014.fctrYes 3.7895513 0.0000000
## Q120194.fctrTry first 0.0000000 0.0000000
## Q120472.fctrArt 0.0000000 0.0000000
## Q120650.fctrNo 0.0000000 0.0000000
## Q120650.fctrYes 3.7720422 0.0000000
## Q120978.fctrNo 0.0000000 0.0000000
## Q120978.fctrYes 0.0000000 0.0000000
## Q121011.fctrNo 0.0000000 0.0000000
## Q121011.fctrYes 0.0000000 0.0000000
## Q121699.fctrNo 7.9896777 0.0000000
## Q121700.fctrNo 1.3552284 0.0000000
## Q121700.fctrYes 2.4722157 0.0000000
## Q122120.fctrNo 0.0000000 0.0000000
## Q122769.fctrNo 0.0000000 0.0000000
## Q122769.fctrYes 0.0000000 0.0000000
## Q122770.fctrNo 0.0000000 0.0000000
## Q122770.fctrYes 0.0000000 0.0000000
## Q122771.fctrPc 0.0000000 0.0000000
## Q123464.fctrNo 2.1880638 0.0000000
## Q123464.fctrYes 0.0000000 0.0000000
## Q123621.fctrNo 0.0000000 0.0000000
## Q123621.fctrYes 0.0000000 0.0000000
## Q124122.fctrNo 3.2593588 0.0000000
## Q124122.fctrYes 0.1724380 0.0000000
## Q124742.fctrNo 4.1882314 0.0000000
## Q124742.fctrYes 0.0000000 0.0000000
## YOB.Age.fctr.C 0.0000000 0.0000000
## YOB.Age.fctr.Q 2.5213728 0.0000000
## YOB.Age.fctr^4 6.2269952 0.0000000
## YOB.Age.fctr^5 0.0000000 0.0000000
## YOB.Age.fctr^6 1.6235574 0.0000000
## YOB.Age.fctr^7 5.6993965 0.0000000
## imp
## Q109244.fctrYes 100.0000000
## Hhold.fctrPKn 36.9944053
## Q109244.fctrNo 33.8868736
## Q115611.fctrYes 33.0857210
## Q113181.fctrYes 25.2568478
## Q113181.fctrNo 20.0124144
## Q116881.fctrRight 15.7181326
## Q115611.fctrNo 15.4468453
## Q101163.fctrMom 10.5659833
## Gender.fctrM 9.3011377
## Q119851.fctrNo 8.2839330
## Q100689.fctrYes 7.1880951
## Q118232.fctrIdealist 6.9222265
## Hhold.fctrMKy 6.3603486
## Q110740.fctrPC 6.3012963
## Q120379.fctrYes 5.6225507
## Q122771.fctrPt 5.4793117
## Q120472.fctrScience 4.5878163
## Q115390.fctrYes 3.5298914
## Q115899.fctrCircumstances 3.5184821
## Q101163.fctrDad 3.5118256
## Income.fctr.Q 3.2961742
## Income.fctr.C 2.9415690
## Q121699.fctrYes 2.5999887
## Q106997.fctrGrrr people 1.9215434
## YOB.Age.fctr.L 1.8101721
## Q108855.fctrYes! 1.7549852
## Edn.fctr.L 1.4939928
## Q120194.fctrStudy first 1.1150559
## Q106389.fctrNo 0.4393390
## Q110740.fctrMac 0.3774074
## YOB.Age.fctr^8 0.3126001
## Q122120.fctrYes 0.3025612
## Q115195.fctrYes 0.2957302
## Q112478.fctrNo 0.2549378
## Q106042.fctrNo 0.1446842
## Q120379.fctrNo 0.1264692
## .rnorm 0.0000000
## Edn.fctr.C 0.0000000
## Edn.fctr.Q 0.0000000
## Edn.fctr^4 0.0000000
## Edn.fctr^5 0.0000000
## Edn.fctr^6 0.0000000
## Edn.fctr^7 0.0000000
## Gender.fctrF 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrPKy 0.0000000
## Hhold.fctrSKn 0.0000000
## Hhold.fctrSKy 0.0000000
## Income.fctr.L 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Income.fctr^6 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrNo 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q101162.fctrOptimist 0.0000000
## Q101162.fctrPessimist 0.0000000
## Q101596.fctrNo 0.0000000
## Q101596.fctrYes 0.0000000
## Q102089.fctrOwn 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102674.fctrYes 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q104996.fctrYes 0.0000000
## Q105655.fctrNo 0.0000000
## Q105655.fctrYes 0.0000000
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrYes 0.0000000
## Q106272.fctrNo 0.0000000
## Q106272.fctrYes 0.0000000
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106993.fctrYes 0.0000000
## Q106997.fctrYay people! 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108855.fctrUmm... 0.0000000
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q108950.fctrRisk-friendly 0.0000000
## Q109367.fctrNo 0.0000000
## Q109367.fctrYes 0.0000000
## Q111220.fctrNo 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
## Q111580.fctrSupportive 0.0000000
## Q111848.fctrNo 0.0000000
## Q111848.fctrYes 0.0000000
## Q112270.fctrNo 0.0000000
## Q112270.fctrYes 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113583.fctrTunes 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrMysterious 0.0000000
## Q114386.fctrTMI 0.0000000
## Q114517.fctrNo 0.0000000
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
## Q114961.fctrYes 0.0000000
## Q115195.fctrNo 0.0000000
## Q115390.fctrNo 0.0000000
## Q115602.fctrNo 0.0000000
## Q115602.fctrYes 0.0000000
## Q115610.fctrNo 0.0000000
## Q115610.fctrYes 0.0000000
## Q115777.fctrEnd 0.0000000
## Q115777.fctrStart 0.0000000
## Q115899.fctrMe 0.0000000
## Q116197.fctrA.M. 0.0000000
## Q116197.fctrP.M. 0.0000000
## Q116441.fctrNo 0.0000000
## Q116441.fctrYes 0.0000000
## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrNo 0.0000000
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116881.fctrHappy 0.0000000
## Q116953.fctrNo 0.0000000
## Q116953.fctrYes 0.0000000
## Q117186.fctrCool headed 0.0000000
## Q117186.fctrHot headed 0.0000000
## Q117193.fctrOdd hours 0.0000000
## Q117193.fctrStandard hours 0.0000000
## Q118117.fctrNo 0.0000000
## Q118117.fctrYes 0.0000000
## Q118232.fctrPragmatist 0.0000000
## Q118233.fctrNo 0.0000000
## Q118233.fctrYes 0.0000000
## Q118237.fctrNo 0.0000000
## Q118237.fctrYes 0.0000000
## Q118892.fctrNo 0.0000000
## Q118892.fctrYes 0.0000000
## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrGiving 0.0000000
## Q119650.fctrReceiving 0.0000000
## Q119851.fctrYes 0.0000000
## Q120012.fctrNo 0.0000000
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrTry first 0.0000000
## Q120472.fctrArt 0.0000000
## Q120650.fctrNo 0.0000000
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrNo 0.0000000
## Q122769.fctrNo 0.0000000
## Q122769.fctrYes 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q123621.fctrYes 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrNo 0.0000000
## Q124742.fctrYes 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^6 0.0000000
## YOB.Age.fctr^7 0.0000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 97
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1309 D 0.2468488
## 2 1788 D 0.2287000
## 3 1311 D 0.2179760
## 4 892 D 0.2694976
## 5 1393 D NA
## 6 4956 D 0.2432875
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 R TRUE
## 5 <NA> NA
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.7531512 FALSE
## 2 0.7713000 FALSE
## 3 0.7820240 FALSE
## 4 0.7305024 FALSE
## 5 NA NA
## 6 0.7567125 FALSE
## Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1 0.2472526 R
## 2 0.2575390 R
## 3 0.2588897 R
## 4 0.2602422 R
## 5 0.2616102 R
## 6 0.2618092 R
## Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1 TRUE 0.7527474
## 2 TRUE 0.7424610
## 3 TRUE 0.7411103
## 4 TRUE 0.7397578
## 5 TRUE 0.7383898
## 6 TRUE 0.7381908
## Party.fctr.Final..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1 FALSE -0.4527474
## 2 FALSE -0.4424610
## 3 FALSE -0.4411103
## 4 FALSE -0.4397578
## 5 FALSE -0.4383898
## 6 FALSE -0.4381908
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 803 5426 D 0.5520684
## 1053 2969 D 0.5223266
## 1435 4997 D 0.5649939
## 1708 6584 D 0.5751797
## 1979 5275 D 0.5839014
## 2281 5638 R 0.7186429
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 803 R TRUE
## 1053 R TRUE
## 1435 R TRUE
## 1708 R TRUE
## 1979 R TRUE
## 2281 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 803 0.4479316
## 1053 0.4776734
## 1435 0.4350061
## 1708 0.4248203
## 1979 0.4160986
## 2281 0.7186429
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 803 FALSE 0.4926216
## 1053 FALSE 0.5176997
## 1435 FALSE 0.5429107
## 1708 FALSE 0.5622760
## 1979 FALSE 0.5932244
## 2281 FALSE 0.7299829
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 803 R TRUE
## 1053 R TRUE
## 1435 R TRUE
## 1708 R TRUE
## 1979 R TRUE
## 2281 D TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 803 0.5073784
## 1053 0.4823003
## 1435 0.4570893
## 1708 0.4377240
## 1979 0.4067756
## 2281 0.7299829
## Party.fctr.Final..rcv.glmnet.is.acc
## 803 FALSE
## 1053 FALSE
## 1435 FALSE
## 1708 FALSE
## 1979 FALSE
## 2281 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 803 FALSE
## 1053 FALSE
## 1435 FALSE
## 1708 FALSE
## 1979 FALSE
## 2281 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 803 -0.20737840
## 1053 -0.18230030
## 1435 -0.15708926
## 1708 -0.13772404
## 1979 -0.10677564
## 2281 0.02998287
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2412 1515 R 0.8615325
## 2413 468 R 0.8760584
## 2414 626 R 0.8747618
## 2415 1236 R 0.8820050
## 2416 2749 R 0.8759471
## 2417 3895 R 0.8943289
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2412 D TRUE
## 2413 D TRUE
## 2414 D TRUE
## 2415 D TRUE
## 2416 D TRUE
## 2417 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 2412 0.8615325
## 2413 0.8760584
## 2414 0.8747618
## 2415 0.8820050
## 2416 0.8759471
## 2417 0.8943289
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2412 FALSE 0.8503119
## 2413 FALSE 0.8503529
## 2414 FALSE 0.8550426
## 2415 FALSE 0.8745067
## 2416 FALSE 0.8753932
## 2417 FALSE 0.8782998
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2412 D TRUE
## 2413 D TRUE
## 2414 D TRUE
## 2415 D TRUE
## 2416 D TRUE
## 2417 D TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 2412 0.8503119
## 2413 0.8503529
## 2414 0.8550426
## 2415 0.8745067
## 2416 0.8753932
## 2417 0.8782998
## Party.fctr.Final..rcv.glmnet.is.acc
## 2412 FALSE
## 2413 FALSE
## 2414 FALSE
## 2415 FALSE
## 2416 FALSE
## 2417 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 2412 FALSE
## 2413 FALSE
## 2414 FALSE
## 2415 FALSE
## 2416 FALSE
## 2417 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 2412 0.1503119
## 2413 0.1503529
## 2414 0.1550426
## 2415 0.1745067
## 2416 0.1753932
## 2417 0.1782998
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final..rcv.glmnet.prob"
## [2] "Party.fctr.Final..rcv.glmnet"
## [3] "Party.fctr.Final..rcv.glmnet.err"
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 21 fit.data.training 9 1 1 468.738 478.282
## 22 predict.data.new 10 0 0 478.283 NA
## elapsed
## 21 9.544
## 22 NAStep 10.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 97## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] 0.7## [1] "glbMdlSelId: All.X##rcv#glmnet"## [1] "glbMdlFinId: Final##rcv#glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glm 0.5848214 0.3425094
## Max.cor.Y##rcv#rpart 0.5633929 0.3774772
## Max.cor.Y.rcv.1X1###glmnet 0.5633929 0.3658672
## Interact.High.cor.Y##rcv#glmnet 0.5625000 0.3664097
## Low.cor.X##rcv#glmnet 0.5553571 0.3187291
## All.X##rcv#glmnet 0.5553571 0.3187291
## Random###myrandom_classfr 0.4696429 0.5191202
## MFO###myMFO_classfr 0.4696429 0.5000000
## Final##rcv#glmnet NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## All.X##rcv#glm 0.5998195 0.5999708
## Max.cor.Y##rcv#rpart 0.5896897 0.6000450
## Max.cor.Y.rcv.1X1###glmnet 0.5896897 0.5721673
## Interact.High.cor.Y##rcv#glmnet 0.5920549 0.5996718
## Low.cor.X##rcv#glmnet 0.6261026 0.6218534
## All.X##rcv#glmnet 0.6261026 0.6218534
## Random###myrandom_classfr 0.5235690 0.4700989
## MFO###myMFO_classfr 0.5000000 0.4700989
## Final##rcv#glmnet NA 0.6290113
## opt.prob.threshold.fit
## All.X##rcv#glm 0.6
## Max.cor.Y##rcv#rpart 0.6
## Max.cor.Y.rcv.1X1###glmnet 0.6
## Interact.High.cor.Y##rcv#glmnet 0.6
## Low.cor.X##rcv#glmnet 0.6
## All.X##rcv#glmnet 0.6
## Random###myrandom_classfr 0.6
## MFO###myMFO_classfr 0.5
## Final##rcv#glmnet 0.6
## opt.prob.threshold.OOB
## All.X##rcv#glm 0.7
## Max.cor.Y##rcv#rpart 0.6
## Max.cor.Y.rcv.1X1###glmnet 0.6
## Interact.High.cor.Y##rcv#glmnet 0.7
## Low.cor.X##rcv#glmnet 0.7
## All.X##rcv#glmnet 0.7
## Random###myrandom_classfr 0.6
## MFO###myMFO_classfr 0.5
## Final##rcv#glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference R D
## R 494 32
## D 466 128## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy 24.52672 4.439736 29.02325 NA
## PKn 54.07667 14.275241 71.20685 NA
## N 170.90149 37.875827 210.90124 NA
## SKn 861.17518 232.827797 1104.95451 NA
## MKn 230.56787 61.957461 294.14171 NA
## SKy 62.64058 23.920386 88.27741 NA
## MKy 575.87008 132.600733 715.28313 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy 0.01169065 0.008035714 0.007183908 52 2 8
## PKn 0.03372302 0.026785714 0.026580460 150 11 26
## N 0.08250899 0.074107143 0.073275862 367 10 92
## SKn 0.43165468 0.456250000 0.458333333 1920 105 533
## MKn 0.11600719 0.121428571 0.121408046 516 23 146
## SKy 0.03304856 0.047321429 0.046695402 147 9 56
## MKy 0.29136691 0.266071429 0.266522989 1296 41 330
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy 9 35 26 10 52 10 61 0.4933040
## PKn 30 131 49 37 150 37 180 0.4758414
## N 83 230 220 102 367 102 450 0.4563353
## SKn 511 1340 1091 638 1920 638 2431 0.4556317
## MKn 136 344 308 169 516 169 652 0.4555696
## SKy 53 119 81 65 147 65 200 0.4513280
## MKy 298 752 842 371 1296 371 1594 0.4449689
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy 0.4716676 NA 0.4757910
## PKn 0.3605111 NA 0.3955936
## N 0.4656716 NA 0.4686694
## SKn 0.4485287 NA 0.4545267
## MKn 0.4468370 NA 0.4511376
## SKy 0.4261264 NA 0.4413870
## MKy 0.4443442 NA 0.4487347
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 1979.758590 507.897181 2513.788095 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 4448.000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## 201.000000 1191.000000 1120.000000 2951.000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 2617.000000 1392.000000 4448.000000 1392.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 5568.000000 3.232979 3.063687 NA
## err.abs.trn.mean
## 3.135840## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes 100.00000 100.000000
## Hhold.fctrPKn 66.66565 36.994405
## Q109244.fctrNo 46.04154 33.886874
## Q115611.fctrYes 42.35760 33.085721
## Q113181.fctrYes 25.05557 25.256848
## Q113181.fctrNo 23.63294 20.012414
## Hhold.fctrMKy 18.99610 6.360349
## Q116881.fctrRight 16.79152 15.718133
## Q115611.fctrNo 16.73937 15.446845
## YOB.Age.fctr.L 16.36530 1.810172
## Income.fctr.C 16.10890 2.941569
## Q122771.fctrPt 16.01287 5.479312
## Edn.fctr^4 14.72729 0.000000
## Hhold.fctrSKy 14.14766 0.000000
## Q119851.fctrNo 13.90035 8.283933
## Q101163.fctrMom 13.86636 10.565983
## Q100689.fctrYes 13.72184 7.188095
## Q118232.fctrIdealist 13.62430 6.922227
## Q120379.fctrYes 13.46387 5.622551
## Q111220.fctrYes 13.11070 0.000000
## Q101163.fctrDad 11.65703 3.511826
## Q110740.fctrPC 11.64944 6.301296
## Q115899.fctrCircumstances 11.14238 3.518482
## Q116881.fctrHappy 10.17271 0.000000
## Q106997.fctrYay people! 10.15641 0.000000
## Q106389.fctrNo 10.05999 0.439339
## Edn.fctr^7 10.03454 0.000000## [1] "glbObsNew prediction stats:"##
## R D
## 1191 201## label step_major step_minor label_minor bgn end
## 22 predict.data.new 10 0 0 478.283 492.695
## 23 display.session.info 11 0 0 492.696 NA
## elapsed
## 22 14.412
## 23 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 2 inspect.data 2 0 0 24.134
## 14 partition.data.training 6 0 0 199.188
## 16 fit.models 8 0 0 311.190
## 17 fit.models 8 1 1 368.632
## 3 scrub.data 2 1 1 162.974
## 20 fit.data.training 9 0 0 438.901
## 22 predict.data.new 10 0 0 478.283
## 1 import.data 1 0 0 10.210
## 18 fit.models 8 2 2 422.512
## 21 fit.data.training 9 1 1 468.738
## 19 fit.models 8 3 3 434.376
## 15 select.features 7 0 0 306.793
## 11 extract.features.end 3 6 6 197.269
## 12 manage.missing.data 4 0 0 198.181
## 13 cluster.data 5 0 0 199.079
## 9 extract.features.text 3 4 4 197.132
## 10 extract.features.string 3 5 5 197.200
## 7 extract.features.image 3 2 2 197.042
## 4 transform.data 2 2 2 196.937
## 6 extract.features.datetime 3 1 1 197.003
## 8 extract.features.price 3 3 3 197.096
## 5 extract.features 3 0 0 196.981
## end elapsed duration
## 2 162.973 138.839 138.839
## 14 306.792 107.604 107.604
## 16 368.631 57.442 57.441
## 17 422.512 53.880 53.880
## 3 196.936 33.962 33.962
## 20 468.738 29.837 29.837
## 22 492.695 14.412 14.412
## 1 24.134 13.924 13.924
## 18 434.376 11.864 11.864
## 21 478.282 9.544 9.544
## 19 438.901 4.525 4.525
## 15 311.190 4.397 4.397
## 11 198.180 0.912 0.911
## 12 199.078 0.897 0.897
## 13 199.188 0.109 0.109
## 9 197.200 0.068 0.068
## 10 197.268 0.068 0.068
## 7 197.095 0.054 0.053
## 4 196.980 0.043 0.043
## 6 197.041 0.038 0.038
## 8 197.131 0.036 0.035
## 5 197.002 0.021 0.021
## [1] "Total Elapsed Time: 492.695 secs"